Reviewerly/exHarmony huggingface dataset:
Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation

NIR-to-RGB spectral domain translation is a challenging task due to the mapping ambiguities and existing methods show limited learning capacities. To address these challenges, we propose to colorize NIR images via a multi-scale progressive feature embedding network (MPFNet), with the guidance of grayscale image colorization. Specifically, we first introduce a domain translation module that translates NIR source images into the grayscale target domain. By incorporating a progressive training strategy, the statistical and semantic knowledge from both task domains are efficiently aligned with a series of pixel-/feature-level consistency constraints. Besides, a multi-scale progressive feature embedding network is designed to improve learning capabilities. Experiments show that our MPFNet outperforms state-of-the-art counterparts by 2.55dB in the NIR-to-RGB spectral domain translation task in terms of PSNR.
Cosine Similarity Based Representation Learning for Adversarial Imitation Learning

Adversarial imitation learning (AIL) aims to recover the reward signal from expert demonstrations and learn expert policy by employing reward and reinforcement learning. However, the raw state-action features of the demonstrations usually have redundant information for a particular control task, and therefore the reward learned from the raw features is often biased, which eventually results in low sample efficiency and instability in AIL. To address these issues, we present CSAIL: Cosine Similarity based Adversarial Imitation Learning. CSAIL extracts expert policy representations from demonstrations via a novel cosine similarity based loss and recovers a robust and unbiased reward function by the learned representations. Based on the reward, CSAIL mimics the expert policy by the Wasserstein distance optimization method. Experimental results show that CSAIL outperforms existing state-of-the-art AIL methods on challenging Mujoco robot control and autonomous driving tasks.
Research on Path Planning Approach of Mobile Robot based on A*-SPF in Complex Environment

Path planning is a hot topic in mobile robots. The path, which planned by A* algorithm, is not smooth. The distance of the path is long, and the fault tolerance of the path is low. Meanwhile, due to the local unknown environments in the global environment, the mobile robot is prone to deviate from the planned path when autonomous navigation is performing. Therefore, the influence of local unknown environments is considered, and a novel path planning approach, which based on A*-SPF, is proposed in this paper. Firstly, according to the unequal rules of environmental information, an improved A* algorithm is proposed by modifying the cost function. Secondly, by integrating a simulated potential field (SPF), a path correction algorithm is proposed, by which the mobile robot can move close to the optimal path. The proposed approach is compared and analyzed in simulation experiments. The results of the experiments demonstrate that the path planned by the proposed approach is accurate. The tracking path is close to the optimal path. The robustness to different environments is also achieved.
How to Select Optimal Parameters for Neighborhood-Based Outlier Detectors?

Neighborhood-based outlier detectors play a vital role in outlier detection, a cornerstone in data science. However, these detectors often rely on their parameters, and finding the optimal values for these parameters can be challenging. To address this issue, we propose a novel approach called K and T Finder using neighborhood consistency (KTF). In KTF, k represents the number of nearest neighbors, and t signifies the threshold value for outlier score thresholding. The core concept behind KTF is rooted in the idea that normal objects should exhibit consistent outlier scores with their neighbors, while outlier objects should display inconsistent outlier scores. Unlike previous approaches where k and t are determined independently, KTF takes a unique approach by simultaneously identifying both parameters. This method computes a consistency value for each combination of k and t, and the optimal values of k and t are chosen by maximizing this consistency value. The experimental results show that the proposed KTF outperforms existing baseline methods.
Agent Based Fetal Face Segmentation for Standard Plane Localization in 3D Ultrasound

In practice, fetal 3D ultrasound can have difficulty in accurately detecting labels for auxiliary standard cut plane localization because of mass loss. Therefore, in this paper, we propose a new segmentation-based reinforcement learning framework for automatically localizing the standard plane of the face: in 3D fetal ultrasound, the initial plane is localized based on anatomical landmarks of mass and geometric relationships, agents navigate through visual segmentation to automatically localize the standard plane, and bound ultrasound views are presented to show the resultant plane. This study was extensively validated on an in-house large dataset. The accuracy of this automatic localization of 3D ultrasound standard planes with sonographer-calibrated median sagittal views of the face, horizontal transverse views of both eyeballs, and coronal views of the nasolabial was 6.64 °/5.65mm, 7.04°/3.58mm, and 5.14 ° 14.26mm, respectively, with success rates of 66.67 %, 78.38 %, and 80.41 %, respectively. The experimental results verify that this system can effectively improve navigation performance.
Spatio-Temporal Attention Based Graph Convolutional Networks for Human Action Reconstruction and Analysis

In recent years, there has been a growing interest in the application of Graph Convolutional Networks (GCNs) for classifying or generating human skeleton-based action sequences. Despite the progress in this field, there exists a relative dearth of research on the underlying mechanisms of how these network structures learn and represent the information features of the human skeleton. This paper proposes a novel GCN-based reconstruction network ST-ATGCN that utilizes spatial and temporal attention mechanisms for analyzing the extraction and reconstruction patterns of human action sequences. This versatile network can be effectively employed in a wide array of applications, including data compression, noise reduction, and interpolation. Experimental results on a public dataset demonstrate that ST-ATGCN network outperforms most of the currently prevailing GCN-based methods. This indicates the efficacy of the proposed network architecture in accurately extracting and reconstructing human skeleton information. Moreover, the reconstruction network exhibits proficiency in effectively restoring noisy action sequences.
Vehicle Detection in Complex Scenes based on YOLOV5

The purpose of the algorithm is to effectively detect vehicles in different conditions, especially in scenes recorded by vehicle recorders. The proposed method is designed to handle complex scenarios with high accuracy and efficiency. Through the use of YOLOV5, the algorithm is able to identify and locate vehicles despite occlusions, density, and low lighting. The intermediate hidden layer's activation function is achieved by using the Leaky ReLU, and data enhancement strategies are utilized to enhance detection performance. By recognizing 100 images of traffic vehicles in complex scenes, the validation results show that the recognition rate of this recognition method is 78.87%, 89.11% and 91.88% for occluded vehicles, crowded vehicles and independent vehicles, respectively. Furthermore, the recognition speed reached 0.012 s/a, which was reduced by 0.005s compared to the original time. All the results demonstrate that the proposed algorithm has a high recognition rate and real-time speed for the complex scenes, indicating that the convolutional neural network has a promising future in the vehicle detection of complex application scenes.
Implementasi Sistem Informasi Notulen Rapat Dan Penugasan Pegawai Pada Dinas Pangan, Tanaman Pangan Dan Hortikultura

The minutes of the meeting are recordings made as a conclusion of the discussions that take place in the meeting process, while the appointment of officials is the assignment of duties to officials in accordance with their respective fields. The current recordings of the minutes of the meeting are still done manually; the minutes are recorded closely at the meeting and are stored in the archive of the notes until they are stacked and vulnerable to damage. The current staff assignment is still done manually by the chief of the service, who makes the staff appointment letter, then prints the staff assignments and gives them to the staff through the staff and general officers department, so it will take quite a long time. The methods used in the minutes of the meeting and the appointment of staff are prototype methods where data collection techniques are used, including interviews and observations, whereas the design methods in this design are described as using case diagrams, activity diagrams, sequence diagrams, and class diagrams. The objective of the research is to transform the manual system into a web-based system that can be accessed online as well as to provide ease in managing the minutes of meetings and staff appointments at the Food Plant and Horticulture Service.
Boundary-Aware Proposal Generation Method for Temporal Action Localization

The goal of Temporal Action Localization (TAL) is to find the categories and temporal boundaries of actions in an untrimmed video. Most TAL methods rely heavily on action recognition models that are sensitive to action labels rather than temporal boundaries. More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries. To address the challenge above, we propose a Boundary-Aware Proposal Generation (BAPG) method with contrastive learning. Specifically, we define the above background frames as hard negative samples. Contrastive learning with hard negative mining is introduced to improve the discrimination of BAPG. BAPG is independent of the existing TAL network architecture, so it can be applied plug-and-play to mainstream TAL models without training. Extensive experimental results on THUMOS14 and ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance of TAL.
A Novel Approach for Repairing Unsegmented Liver Vascular Images based on Centerline

In liver vascular intervention surgery, the absence or narrowing vascular images may impact the understanding of patients’ vascular structure and morphology. Repairing and analyzing vascular images is a crucial task in computer-aided diagnosis and surgery for minimally invasive vascular diseases. Previous research has addressed the problem of repairing small areas with regular shapes or minor curvature changes in the holes, often leading to modifications to the existing points and encounter noise and geometric loss. Unlike existing point cloud completion algorithms, this paper aims to address the issue of missing vessels in liver vascular images that have not been segmented. It employs axis-aligned bounding boxes to extract missing vascular images. Based on the geometric characteristics of the liver vascular centerline, two methods of cross-section and spherical wave propagation are proposed for repair process. These methods take localized missing vascular images as input and preserve the complete topological structure of the vessels. The repair results are evaluated using the Chamfer distance and Dice similarity coefficient. Experimental results demonstrate that the proposed approach effectively repairs large-scale, non-closed missing vascular images in liver vascular images and generates complete vessel geometric models with accurate liver vascular structure and morphology. The validity of the vessel models is confirmed through hemodynamic analysis, providing robust support for medical image analysis and diagnosis.
Deep Modular Fuzzy Inference Model

In recent years, explainable AI (XAI) has been emphasized in various fields such as healthcare, finance, etc. Since the fuzzy rules in fuzzy inference models are structured as If-Then rules, the inference process of fuzzy inference models can be easily understood by humans. Among them, a deep TSK fuzzy inference model has fuzzy rules with low complexities due to select randomly features and map randomly them to fuzzy partitions. However, this randomness may lead to situations where important features or fuzzy partitions of them that have a significant impact on the results are not used in the inference. Therefore, this paper propose a deep modular fuzzy inference model with high accuracy and stability using small number of fuzzy rules with high interpretability.
Federated Learning in Competitive EV Charging Market

Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations’ operation in terms of price competition has yet to be fully understood. This paper aims to fill this gap by modeling the strategic interactions between two charging stations and EV owners as a multi-stage game. Each station first decides its FL participation strategy and charging price, and then individual EV owners decide their charging strategies. The game analysis involves solving a non-concave problem and by decomposing it into a piece-wise concave program we manage to fully characterize the equilibrium. Based on real-world datasets, our numerical results reveal an interesting insight: even if FL improves QoS, it can lead to smaller profits for both stations. The key reason is that FL intensifies the price competition between charging stations by improving stations’ QoS to a similar level. We further show that the stations will participate in FL when their data distributions are mildly dissimilar.
Nonlinear Dynamics of Neural Networks: Applications in Pattern Recognition

Improvements in pattern recognition systems are best achieved by fully comprehending and utilizing the nonlinear dynamics of neural networks. When dealing with complex, nonlinear patterns in real-world data, a nuanced approach is necessary to fully utilize neural networks' potential. By improving performance, interpretability, and flexibility in complicated pattern recognition tasks, Nonlinear Dynamics method meet this demand. Problems with interpretability, complicated training, and generalization in the presence of noisy data are all aspects of nonlinear dynamics that pose difficulties. These complex patterns are difficult for traditional neural networks to capture and comprehend. In response to these difficulties, Nonlinear Dynamics has been created to offer a comprehensive approach to making good use of nonlinear dynamics in pattern recognition. This research presents a novel method for dealing with problems caused by nonlinear dynamics, called Nonlinear Dynamics-Driven Adaptive Neural Network (ND-ANN). To achieve the sweet spot between model complexity and interpretability, NDANN employs a hybrid learning algorithm (HLA), an explainability module, ensemble integration, regularization for stability, and an adaptive architecture. This novel approach lays the groundwork for future developments in pattern recognition technology and guarantees better performance when capturing complicated patterns. Improved pattern recognition systems made possible by NDANN's accurate modeling of nonlinear dynamics pave the way for new developments in healthcare, information processing, and technology. Validation of NDANN's effectiveness in dealing with nonlinear dynamics issues was achieved through extensive simulation analyses. Precision, accuracy, recall, and F1 score are some of the performance indicators that undergo thorough evaluation across various datasets. The inclusion of nonlinear dynamics in neural networks has the ability to transform pattern recognition, and the simulation results show that NDANN is better than traditional models.
Effectiveness of the Utilized Interview Techniques to Enhance Speaking Skills of Students

Speaking is regarded as one of the most difficult aspects of language learning because learners find it difficult to express themselves and are affected by factors that led them to be reluctant to speak. Hence, this study 1. determined the factors affecting the skills of the students in speaking English. 2. Documented the utilization of the interview techniques to enhance the speaking skills of the students and discussed the challenges encountered and 3. Analyzed the perceived effectiveness of the utilized interview techniques to enhance the students' speaking confidence. The study used a mixed-methods approach using pre- and post-surveys, interviews, and group discussions. The respondents were the 22 Grade 10-Abad students of Tinago National High School. Results showed that students struggle to speak English because of the following factors: pronunciation, vocabulary, fear of public speaking, criticism, and shyness. Using criteria as an assessment tool, it revealed that there was an improvement of 20% in the students’ English-speaking skills when the interview techniques of PREP and STAR methods were used as an activity, concluding that the usage of utilized interview techniques enhanced the English-speaking skills of students.
Discussion on the Utilization and Improvement of Human Trafficking Victim Identification Indicators: Focusing on the perception of the workers of the Organizations utilizing identification indicators

Since January 1, 2023, the “Act on the Prevention of Human Trafficking and Protection of Victims” has expanded the concept of human trafficking to protect and support victims such as labor exploitation, sexual exploitation, and prostitution. The law recommends the use of human trafficking identification indicators along with human trafficking education to detect crimes such as human trafficking and identify potential victims. Therefore, this study attempted to review human trafficking identification indicators and discuss improvement measures for effective use at the time when the law was enforced a year later. To this end, a survey was conducted on 150 employees of the National Police Agency, the Prosecutor's Office, the Ministry of Employment and Labor, and the Ministry of Oceans and Fisheries, which recommended the use of legal identification indicators, and the contents of the collected data were classified and analyzed. Through the study, the necessity of mandatory identification education and expanding the target, digitizing reporting and identification indicators, subdividing identification indicators for each target, and reorganizing other systems was discussed.
Pengaruh Multi Leaf Collimator (MLC) Terhadap Besar Dosis Yang Diterima Bagian Kepala Pasien Kanker Otak

Research has been conducted on the effect of Multi Leaf Collimator (MLC) on the dose received by the head of brain cancer patients at Sanglah Hospital, Denpasar. This analysis aims to determine the effect of using MLC on dosing to brain cancer patients using Cobalt 60 (Co-60) teletheraphy. The dose given at TPS was 200 cGy but after using MLC the dose was received because MLC protects healthy organs around the irradiation field. If the dose given is still within the range of -5 % and +7 %, it is still acceptable. The amount of radiation dose that can be reduced by using MLC in this study was 1,35 % for brain cases and 0,12 % for Glioblastoma cases.
UPAYA DIGITALISASI NASKAH KUNO DI PERPUSTAKAAN YAYASAN PANGERAN SUMEDANG

Indonesia is recognized as a country with the greatest wealth of ancient manuscript heritage in the world. The diversity of cultures and customs stretching from Sabang to Merauke makes Indonesia has hundreds or even thousands of ancient manuscripts. Today, ancient manuscripts are considered the nation's cultural heritage with high historical value and their existence is stored as carefully as possible in libraries and museums. Even so, writing that is tens or even hundreds of years old makes ancient manuscripts vulnerable to damage. This cannot be ignored because ancient manuscripts have a source of information and real evidence of the past civilization of the nation's ancestors. Therefore, it is necessary to take concrete action to preserve ancient manuscripts, one of which is by media transfer or digitalization methods. This qualitative research focuses on ancient manuscripts stored in the Yayasan Pangeran Sumedang library. Including 4 informants who have background knowledge regarding the preservation of ancient manuscripts. The results of this research show that the Yayasan Pangeran Sumedang library is digitizing as an ancient manuscript preservation activity. In its implementation, the library applies standard operational steps in media transfer activities, namely (1) Pre-digitization, (2) Digitization, and (3) Post-digitization. This research shows that the final product of the digital media transfer activities carried out at the Yayasan Pangeran Sumedang library is an e-book. This digital version of ancient manuscripts is intended for all levels of society, with the hope that ordinary people can access the knowledge contained in ancient manuscripts. Not only that, the repackaging of this ancient manuscript also targets the younger generation to become increasingly interested in reading the history of the nation's ancestors
A 5G noise and interference power estimation method based on the fusion of time-frequency acquisition information

With the advancement of digital power grid construction, digital operation and inspection services such as video surveillance, online monitoring of main and auxiliary equipment, and mobile inspection are increasingly carried by wireless communication technologies such as 5G. Before the construction and application of wireless communication networks in various scenarios of electric power, it is necessary to collect the spectrum information of the frequency band to be occupied on-site through 5G quality assessment and analysis equipment for noise and interference analysis. In this paper, a method of estimation of the floor noise power based on the fusion of the level acquisition information in the time-frequency domain is designed, which can effectively estimate the floor noise power. Since the floor noise and colored interference are independent of each other and their power conforms to the additive law, the existence of colored interference can be effectively determined according to the estimated floor noise power. Based on the method proposed in this paper, the noise power at the bottom of the 5G working band can be accurately evaluated, which provides a strong basis for determining whether there is interference, and the next step of interference positioning, wireless communication technology selection, and network planning.