Reviewerly/exHarmony huggingface dataset:
Research on Unrelated Parallel Machine Scheduling Problem Considering Machine Eligibility

Consider an unrelated parallel machine scheduling problems with machine eligibility, where each processed workpiece can only be arranged on specific machines for processing. In this paper, we propose a particle swarm optimization (PSO) algorithm which introduces an elite reverse learning strategy and sine and cosine optimization algorithm to minimize the maximum completion time. In this algorithm, the elite reverse learning strategy is introduced heuristically to provide better feasible solutions for the algorithm randomization, thus accelerating the convergence rate. In addition, the sine and cosine optimization algorithm is introduced in the particle iteration process to update the velocity vector of the particle swarm, so as to enhance the diversity of the population. The effectiveness and advantage of the new PSO in solving the problem are verified by simulation experiments and comparation with other algorithms.
Experimental Investigation of Side-Channel Information Leakage from Printed Circuit Board with Split Ground Planes

Side-channel attacks, which break encryption by analyzing the physical behavior leaked from cryptographic devices, have become information security threats. This paper experimentally studied a standard evaluation board for side-channel attacks, SASEBO-G, and identified the source of side-channel information leakage superimposed on the common-mode (CM) current. Regarding the source of the CM current, we examined the effects of split ground planes for cryptographic and control FPGAs and an imbalance difference between SASEBO-G and power cables. We observed CM currents flowing through the cables. The correlation power analysis was performed by changing the separation of the ground plane and the amount of mode conversion caused by the imbalance difference. As a result, the CM current and information leakage intensity varied significantly depending on the ground plane separation. The ground separation on SASEBO-G is a potential cause of side-channel information leakage superimposed on the CM current.
Transformer Based Multi-model Fusion for 3D Facial Animation

The topic of 3D drivable facial animation has been extensively studied for many years. Due to the limited audiovisual data and the uncertainty of the problem, it is still challenging to generate faithful facial motions. Most existing research focuses mainly on using audio information, which easily leads to excessively smooth lip movements during the generation. Considering the progress of multimodal learning and the advantages of phoneme-based mapping-driven approaches. We combine the phoneme and text representation into the task to enhance the relationship between speech and animation. Specifically, we present a novel multi-model fusion Transformer for the 3D facial generation that learns the features from audio, text, and phonemes modality. Utilizing the intrinsic relationship between phonemes and corresponding mouth movements can constrain excessive smoothness in the mapping from audio to mouth movements. Using powerful language representation models to obtain contextual semantic information from the text can eliminate facial animation biases caused by individual speaking characteristics in the audio. Additionally, to explore the rich information between modalities, we also introduce an attention-based fusion mechanism to integrate related features. The qualitative and quantitative experiments proved our method’s effectiveness.
Attention-based Self-Supervised Hierarchical Semantic Segmentation for Underwater Imagery

This work presents a method for semantic segmentation of underwater visual data. By leveraging recent advances in the machine learning community, this work shows that it is possible to overcome the lack of large labeled datasets for underwater scenes by using a self-supervised learning approach that can be pretrained on a large terrestrial dataset before being fine-tuned on a smaller underwater dataset. Results are provided demonstrating the effectiveness of this approach on a publicly available underwater dataset, as well as on experimentally collected data. Furthermore, this approach is shown to have a notable side effect in that multiple levels of detail for objects can be segmented out within a single forward pass through the network.
Fuzzy retrieval algorithm for film and television animation resource database based on deep neural network

The large amount of data in the film and television animation resource library leads to the increase of retrieval difficulty and the decrease of retrieval accuracy. Therefore, a fuzzy retrieval algorithm for film and television animation resource database based on deep neural network is designed. According to the structure of the film and television animation resource base and the static and dynamic characteristics of the color, texture and shape of the film and television animation, the deep neural network is used to supervise and learn the multi-layer neural network of the film and television animation and extract the characteristics of the film and television animation. The improved fuzzy C-means is used for feature clustering analysis. According to the results of feature clustering analysis, the fast sorting algorithm and Top-k sorting algorithm are used to obtain the retrieval results of the film and television animation resource library, and improve the retrieval accuracy of the film and television animation resource library. The experiment shows that using the 7th layer features of deep neural networks for animation resource retri of 90.15%. Therefore, this algorithm eval has the highest recall rate of 98% and the highest average precision rate meets the retrieval requirements of film and television animation resource libraries and can significantly improve the comprehensive performance of film and television animation resource library retrieval.
A weakly supervised multi-instance learning based on graph neural network for breast cancer pathology image classification

Breast cancer is a malignant tumor that occurs in the breast, posing a serious threat to women’s health. Histopathological examination is an important means of breast cancer diagnosis, and the analysis of histopathological images is an important application of artificial intelligence in histopathological examination. Despite the remarkable achievements of convolutional neural networks (CNNs), there are still challenges in distinguishing between multiple subtypes and overcoming imbalanced datasets. Therefore, to overcome these challenges, this paper proposes a weakly supervised multi-instance learning model GMIL based on graph neural networks (GNNs) for breast cancer pathology image classification. The model consists of three modules: the feature extraction module for feature extraction, the GNN module for aggregating neighbor information, and the gated attention module for feature fusion. Multiple experiments on the BreakHis dataset show that the proposed model outperforms the latest algorithms in terms of performance.
Lidar-based Human Detection for a Mobile Robot in Social Environment using Deep Learning

Nowadays, robotics implementation spans industries such as manufacturing, healthcare, and logistics, integrating AI-driven capabilities to enhance efficiency and collaboration in diverse operational environments. The ability to respond and interact while considering human presence is crucial for a mobile robot to operate effectively and be accepted in a human social environment. It is necessary to make a robot that can detect the presence of humans around it to make it capable of navigating while considering the social environment. Meanwhile, considering that the lidar sensor is almost always available in many robot navigation cases, it becomes an accessible tool for detecting the presence of humans in its surroundings. We brought the one-dimensional lidar data into two-dimensional image data, then involved some object selection based on weak characteristics, such as the acceptable range radius of the object and the acceptable distance between two legs. The result of the process is a group of human candidates. We use a deep learning model with a pretrained mobilenetv3 model as the feature extractor to ensure whether the candidates are humans and a fully connected layer of neural networks as the classifier. We defined several scenarios in an indoor environment to test the performance of the proposed algorithm. The experiment results show the system’s performance with a precision score of 0.902, a recall score of 0.782, and an F1 score of 0.837.
MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding

Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we present MarineVRS, a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. In addition, unlike the conventional video retrieval system, which only permits users to index a collection of images or videos and search using a freeform natural language sentence, our retrieval system includes an additional Explainability module that outputs the segmentation masks of the objects that the input query referred to. This feature allows users to identify and isolate specific objects in the video footage, leading to more detailed analysis and understanding of their behavior and movements. Finally, with its adaptability, explainability, accuracy, and scalability, MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species.
Multi-label Video Classification for Underwater Ship Inspection

Today ship hull inspection including the examination of the external coating, detection of defects, and other types of external degradation such as corrosion and marine growth is conducted underwater by means of Remotely Operated Vehicles (ROVs). The inspection process consists of a manual video analysis which is a time-consuming and labor-intensive process. To address this, we propose an automatic video analysis system using deep learning and computer vision to improve upon existing methods that only consider spatial information on individual frames in underwater ship hull video inspection. By exploring the benefits of adding temporal information and analyzing frame-based classifiers, we propose a multi-label video classification model that exploits the self-attention mechanism of transformers to capture spatiotemporal attention in consecutive video frames. Our proposed method has demonstrated promising results and can serve as a benchmark for future research and development in underwater video inspection applications.
Mission Planning for Collaborative Operations of Autonomous Marine Systems

Autonomous systems consisting of multiple surface and underwater vehicles are capable of performing long-term, high level tasks through cooperation. However, achieving reliable autonomy under substantial uncertaies and environmental changes requires a system that can recognize the situation and make high-level decisions. This study presents a mission planner developed using Planning Domain Definition Language (PDDL) with a replanning structure that incorporates three methods to enhance both optimality and robustness. The planner employs three methods to achieve this: the greedy iterative method improves the planner’s optimality, the model adaptive reduces the gap between the environment and the model to increase operational performance, and the reserved enhances mission success rates by allowing the planner to adapt to changes in the environment. We demonstrate that the proposed mission planner can provide an efficient and stable plan to complete a mission.
Autonomous Underwater Vehicles Identification through a Kernel Regressor

A kernel regressor to estimate a six-degree-of-fredoom non linear model of an autonomous underwater vehicle is proposed. Although this estimator assumes that the model coefficients are linear combinations of basis functions, it circumvents the problem of specifying the basis functions by using the kernel trick. The Gaussian radial basis function is the chosen kernel, with the Kernel matrix being regularized by its principal components. The variance of the Gaussian radial basis function and the number of principal components are hyper-parameters to be determined by the minimisation of a final prediction error criterion and using the training data. A simulated autonomous underwater vehicle is proposed was used as case study.
Obstacle Detection and Tracking System for Robot Soccer ERSOW based on Centroid Tracking Method

EEPIS Robot Soccer on Wheeled (ERSOW) is a wheeled soccer robot developed by Politeknik Elektronika Negeri Surabaya and participated in the national robot competition, Indonesian Robot Contest in KRSBI Wheeled division. ERSOW team has striker robot, striker robot was used in attack strategy for scoring goals. The ability to tracking the obstacle is one of main abilities that striker robot must has. Obstacle tracking plays a crucial role in robot soccer on wheels due to the dynamic and unpredictable nature of the game. As robots navigate the field, they encounter various obstacles such as opponents, teammates, and static objects (obstacle). Accurate and real-time tracking of these obstacles enables robots to make informed decisions, adapt their strategies, and effectively interact with the environment. Obstacle tracking enhances situational awareness. By continuously monitoring the positions and movements of obstacles, robots gain valuable information about the current game state. Tracking system on this research works with centroid tracking algorithm. The fundamental concept of the centroid tracking algorithm revolves around assigning a distinct identification (ID) to every tracked object by taking their centroids, this algorithm takes the center of obstacle bounding box as an input, to generated bounding box this system used thresholding detection method based on HSV color space. The center of bounding box (in pixel coordinate) converted into world coordinate by using Ab-exponential regression and Linear regression. The testing results of tracking system could estimate obstacle from 50cm–250cm from the robot with 0,967 cm error average, the system also could track the obstacle with 86% accuracy when the robot moved 330 cm ahead.
Gamification Interaction Design for Membatik Application in Mixed Reality

Batik is an Indonesian traditional craft that has high artistic value. Indonesian society, especially the younger generation such as students, plays an important role in preserving Indonesian culture such as batik to ensure it remains attached to Indonesia and does not disappear. However, the interest and enthusiasm of students in preserving batik is still lacking, so new strategies are needed to increase their interest and enthusiasm in preserving batik. One of the new strategies is utilizing immersive technology such as Mixed Reality (MR) with the implementation of gamification interaction to enhance students’ interest because learning is done as if playing a game. MR technology can provide users with a more complex experience by using MR glasses. The Membatik application is a batik-making simulation application using MR technology where users can simulate with hand gestures and there are quizzes at each stage with a point system as a gamification implementation. The application has been tested with initial user testing. During the testing phase, users were asked to try the application and provide feedback on the overall user experience. The outcomes demonstrated that the MR application might raise students’ interest in and involvement in their study. The users also found the quiz feature to be interactive and challenging, which added to their learning experience. By combining gamification elements with MR technology, the application has the potential to improve learning outcomes and increase student motivation.
Coordination of Marine Multi-Vehicle Autonomous Systems via the Distributed Command Governor Approach

This paper investigates motion coordination problems for a set of dynamically decoupled omnidirectional Unmanned Surface Vehicles (USVs) subject to local, obstacle, and collision avoidance constraints. An existing supervision scheme based on Distributed Command Governor ideas is here extended to achieve vehicles’ motion coordination. More specifically, the extension consists to add the ability to perform Plug-and-Play (PnP) operations and fulfilling collision avoidance constraints among agents operating in the same 2D space. Ensuring collision avoidance is paramount for performing missions where many agents share the same space. Obstacle avoidance constraints are satisfied as well at each time instant by using a separation hyperplane and solving a linear optimization problem online with a mixture of continuous and integer variables. Finally, some examples are presented to illustrate the effectiveness of the proposed strategy.
Systematic Literature Review Langchain Proposed

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review.
Experience the Theory: New Perspectives Through VR Learning Environments for Photography Education

This paper presents an in-depth exploration of the development and application of a Virtual Reality (VR)-based photo studio, specifically designed for the education of photography. Given the inherent nature of photography, which necessitates finding suitable perspectives within a three-dimensional space, it was crucial to achieve a photorealistic implementation. This implementation encompasses aspects of spatial perception, lighting ambiance, and camera interactions. In addition to these technical aspects, the system integrates explanatory, motivational, and feedback systems. These additional features are pivotal in aiding learners in the acquisition of both technical proficiencies and creative skills, fostering a conducive environment for independent learning and self-directed activity. One of the key features of this VR-based photo studio is the real-time simulation of adjustable camera parameters. This includes a virtual camera and the visualization of their effects on image perception, such as the use of varying focal lengths and depths of field. These features provide learners with an immersive experience that allows them to gain practical knowledge and skills that can be applied to scenarios outside the virtual environment. This hands-on approach to learning not only enhances understanding but also promotes retention of knowledge and skills. The paper concludes with an initial user study, providing valuable insights into the effectiveness and potential areas of improvement for this innovative approach to photography education. This study serves as a stepping stone for future research and development in this field, paving the way for the evolution of VR in education.
Symbolic Quantum Programming for Supporting Applications of Quantum Computing Technologies

The goal of this paper is to deliver the overview of the current state of the art, provide experience report on developing quantum software tools, and outline the perspective for developing quantum programming tools supporting symbolic programming for the needs of quantum computing technologies. The main focus of this paper is on quantum computing technologies, as they can in the most direct way benefit from developing tools enabling the symbolic manipulation of quantum circuits and providing software tools for creating, optimizing, and testing quantum programs. We deliver a short survey of the most popular approaches in the field of quantum software development, pointing their strengths and weaknesses. This helps to formulate a list of desirable characteristics which should be included in quantum computing frameworks. Next, we describe a software architecture and its preliminary implementation supporting the development of quantum programs using symbolic approach, encouraging the functional programming paradigm, and, at the same, time enabling the integration with high-performance and cloud computing. The described software consists of several packages developed to address different needs, but nevertheless sharing common design concepts. We also outline how the presented approach could be used in tasks in quantum software engineering: quantum software testing and quantum circuit construction.
Underwater Image Restoration via Machine Learning Transmission Map of Atmospheric Scattering Model

Traditionally, image dehazing methods must rely on employing the Atmospheric Scattering Model (ASM) to perform image restoration. These methods are characterized in that two key parameters: atmospheric light A(x) and Transmission t(x) of the entire image are either assumed (but often inaccurately) or heuristically estimated, However, the estimation of A(x) and t(x) is non-trivial. In particular, since (x) varies with the scene depth, making ASM not attractive, especially for underwater applications wherein different wavelengths of light are absorb to different degrees. In this paper, we propose a novel approach which is different from traditional counterparts in that t(x) is learned directly using color features of hazed image as training input, and t(x) of its corresponding clear image as target output for training deep learning network. Our method comprises three steps: First, the input feature vectors are prepared by concatenating the global and local color features of the training and test input images. Second, the color features are standardized and subjected to training. Finally, prior to image restoration, the output $\hat t(x)$ of the trained network is blurred using the locality property. Using a six-layer neural network [32,128, 256,128, 32, 1] as an illustrative example, our method outperforms Fusion-based [1], Retinex-based [2], IBLA [3], AGC [4], GDCP [5], UWCNN [6], BL-TM [7], DehazeNet [8], in terms of contrast, sharpness, as well as the overall restoration appearance.