Reviewerly/exHarmony huggingface dataset:
PENILAIAN KINERJA DAN PENANGANAN SISTEM IRIGASI PADA DAERAH IRIGASI UBUD BALI

Ubud irrigation area is one of the irrigation areas, located in the Oos Watershed, Gianyar Regency, Bali. The successful performance of the irrigation system is highly dependent on the management of the irrigation system. It is reflected in the ability to support the system operation providing sufficient water for a conducive implementation of the crop pattern in the service area. It is very important to evaluate the performance of the irrigation system to monitor the condition and performance of all aspects of the irrigation system. The purpose of this study is to obtain a performance index assessment of the primary and secondary networks of the Ubud irrigation. The priority for handling irrigation networks is determined using the decision-making method using the Analytic Hierarchy Process (AHP) Method. Calculation of cost for operation and maintenance (AKNOP) is also implemented. The performance index assessment in the Ubud Irrigation Area obtained is 72.22% consisting of 32.36% physical infrastructure; planting productivity 11.31%; supporting facilities 6.47%; personnel organization 10.75%; documentation 3.40%; and associations of farmers using water 7.93%. Priority determination of irrigation assessment using the AHP method returns the aspects of the physical infrastructure at the first priority rank. The sub aspect of structure on the channel under the physical infrastructure aspect is selected to be handled due to the highest percentage of damage of 32.15%. The AKNOP value of structures on the channel in the Ubud Irrigation Area is Rp. 391,078,799.00. Implementation of maintenance activities consists of routine and periodic maintenance, which are light maintenance and repairs.
Statistical analysis of cloud layers and solar irradiations for all seasons in Toyohashi city, Japan

Several solar irradiation forecasting methods are being developed toward economically efficient solar power generation. Among these, the method that utilizes all sky imagers to estimate the direction of cloud movement above certain observation points is able to forecast fluctuations in solar irradiation with localized temporal resolution (∼30 min) through observation of shadows cast by clouds. The one-layered cloud is usually assumed in forecasting the direction of cloud movement. However, in reality, multilayered clouds may move in different directions in the sky. While such multilayered clouds may have effect on forecast accuracy, the extent to which multilayered clouds affect the solar irradiation and the generated solar power has never been reported. Therefore, the purposes of this study are (1) to give the dataset to know the effect of multilayered clouds and (2) to decide whether the multilayered clouds have to be considered in forecasting of solar irradiation. We analyzed the number of cloud layers and their effect on the solar irradiation, the generated solar power, and the clearness index through statistical analysis for all seasons in Toyohashi city, Japan. The dataset used to analysis includes data with 4381 h. The analytical results show that multilayered clouds were observed for ∼40% of the daytime duration even in winter. In addition, the results show that multilayered clouds were observed even when the solar irradiation, the generated solar power, and the clearness index were high, which suggests that the effect of multilayered clouds must be considered. In addition, it will be shown that the maximum number of cloud layers to be considered is 3 from statistical analysis.
Implementing a FAIR Information System for Archaeology-Related Interdisciplinary Research

Within the DFG funded, archaeology-focused project Collaborative Research Center 1266 (Scales of Transformation) an information system is implemented including roles, processes, hardware and software infrastructure with the goal to collect data and meta data available, integrate the data for (re)presentation, exploration as well as analysis, and therefore support a project-wide data-driven synthesis. Several challenges arise from the diversity of people coming together and the heterogeneous data environment present over all the disciplines involved. This poster illustrates these challenges, what it takes to bridge the gaps between differing perspectives and briefly proposes approaches involved to successfully address them.
Dual-Path Transformer for 3D Human Pose Estimation

Video-based 3D human pose estimation has achieved great progress, however, it is still difficult to learn precise 2D-3D projection under some hard cases. Multi-level human knowledge and motion information serve as two key elements in the field to conquer the challenges caused by various factors, where the former encodes various human structure information spatially and the latter captures the motion change temporally. Inspired by this, we propose a DualFormer (dual-path transformer) network which encodes multiple human contexts and motion detail to perform the spatial-temporal modeling. Firstly, motion information which depicts the movement change of human body is embedded to provide explicit motion prior for the transformer module. Secondly, a dual-path transformer framework is proposed to model long-range dependencies of both joint sequence and limb sequence. Parallel context embedding is performed initially and a cross transformer block is then appended to promote the interaction of the dual paths which improves the feature robustness greatly. Specifically, predictions of multiple levels can be acquired simultaneously. Lastly, we employ the weighted distillation technique to accelerate the convergence of the dual-path framework. We conduct extensive experiments on three different benchmarks, i.e., Human 3.6M, MPI-INF-3DHP and HumanEva-I. We mainly compute the MPJPE, P-MPJPE, PCK and AUC to evaluate the effectiveness of proposed approach and our work achieves competitive results compared with state-of-the-art approaches. Specifically, the MPJPE is reduced to 42.8mm which is 1.5mm lower than PoseFormer on Human3.6M, which proves the efficacy of the proposed approach.
Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation

Semantic communication (SemCom) is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods of existing SemCom frameworks are inexplicable and inflexible, which limits the achievable performance. In this paper, to tackle this issue, a knowledge graph (KG) is exploited to develop SemCom systems. Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. Moreover, a simple, general, and interpretable semantic alignment algorithm for semantic information detection is proposed. Furthermore, an effective semantic correction algorithm is proposed by mining the inference rule from the KG. Additionally, the pre-trained model is fine-tuned to recover semantic information. For the multi-user cognitive SemCom system, a message recovery algorithm is proposed to distinguish the messages of different users by matching the knowledge level and the context at the destination. Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive SemCom systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability. Finally, we present realistic single-user and multi-user cognitive SemCom systems results by building a software-defined radio prototype system.
Online Power System Event Detection via Bidirectional Generative Adversarial Networks

Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. The accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.
Sign Language Recognition with Multimodal Sensors and Deep Learning Methods

Sign language recognition is essential in hearing-impaired people’s communication. Sign language recognition is an important concern in computer vision and has been developed with rapid progress in image recognition technology. However, sign language recognition using a general monocular camera has problems with occlusion and recognition accuracy in sign language recognition. In this research, we aim to improve accuracy by using a 2-axis bending sensor as an aid in addition to image recognition. We aim to achieve higher recognition accuracy by acquiring hand keypoint information of sign language actions captured by a monocular RGB camera and adding sensor assist. To improve sign language recognition, we need to propose new AI models. In addition, the amount of dataset is small because it uses the original data set of our laboratory. To learn using sensor data and image data, we used MediaPipe, CNN, and BiLSTM to perform sign language recognition. MediaPipe is a method for estimating the skeleton of the hand and face provided by Google. In addition, CNN is a method that can learn spatial information, and BiLSTM can learn time series data. Combining the CNN and BiLSTM methods yields higher recognition accuracy. We will use these techniques to learn hand skeletal information and sensor data. Additionally, the 2-axis Bending sensor glove data support training AI model. Using these methods, we aim to improve the recognition accuracy of sign language recognition by combining sensor data and hand skeleton data. Our method performed better than using skeletal information, achieving 96.5% accuracy in Top-1.
An encoder-decoder generative adversarial network-based anomaly detection approach for satellite telemetry data

Telemetry anomaly detection is a prominent health condition monitoring task that plays an increasingly crucial role in discovering potential incidents and facilitating long-term reliable on-orbit operations of satellite. Nevertheless, multiple monitored telemetry parameters and the complicated correlations among them pose significant challenges. In this study, an encoder-decoder generative adversarial network (EDGAN)-based anomaly detector is proposed for detecting multivariate anomalies in telemetry data. Firstly, an additional encoder is merged into the generator to learn a latent representation of the multivariate telemetry data for generating a reasonable and enhanced substitute of the data. Secondly, a feature matching strategy is applied in the discriminator to facilitate extracting more distinguishable features. Thirdly, three loss functions are designed for model training, and the deviation from the data space of the generator is utilized as the criterion to detect anomalies. Finally, experiments on real-world and public satellite datasets are conducted and the results verify the effectiveness of the proposed method.
Full stage networks with auxiliary focal loss and multi-attention module for submarine garbage object detection

Submarine garbage is constantly destroying the marine ecological environment and polluting the ocean. It is critical to use detection methods to quickly locate and identify submarine garbage. The background of submarine garbage images is much more complex than that of natural scene images, with object deformation and missing contours putting higher demands on the detection network. To solve the problem of low accuracy under complex backgrounds, full stage networks with auxiliary focal loss and multi-attention module are proposed for submarine garbage object detection based on YOLO. To maximize the gradient combination, a hierarchical fusion feature mechanism and a segmentation and merging strategy are used in this paper to optimize the difference in gradient combination to obtain full-stage features. Then the criss-cross attention module is used to precisely extract multi-scale features of small object dense regions while removing noise information from complex backgrounds. Finally, the auxiliary focal loss function addresses the issue of unbalanced positive and negative samples, focusing on the learning of difficult samples while improving overall detection precision. Based on comparative experiments and ablation experiments, the FSA networks achieved state-of-the-art performance, and is applicable to the real-time object detection of submarine garbage in complex backgrounds.
YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations

With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.
The New Robust Adaptive Median Filter for Denoising Cancer Images Using Image Processing Techniques

Background/Objectives: One of the leading causes of death for women is breast cancer, and extensive research has been conducted to improve the diagnosis and detection of breast cancer using various image processing techniques. Medical imaging plays a crucial role in this domain, particularly mammography, which is widely used for breast cancer screening and diagnosis. This paper introduces a novel filtering technique called the New Robust Adaptive Median Filter (RAMF). Method: The suggested approach only takes into account noise-free pixels when determining the window’s median. The median is computed from the remaining pixel values when the ◦ or 255 pixel values are excluded. In order to filter high densities of salt-andpepper noise, the adaptive windowing approach is applied, which enables our algorithm to extend the size of its filtering window dependent on the local noise density. Moreover, a threshold value is employed to establish the pixel value under extreme circumstances, such as pure black and white photos with noise. Finding: To compare filters based metrics, we find evaluate the filters using a standardized dataset and calculate the MSE, PSNR, and UQI values for each filter. These values can then be compared to determine which filter performs better in terms of noise reduction and image quality enhancement. The Proposed Filter shows good performance in low and higher density ranges (10%-90%) to effectively reduce noise in higher density values. Novelty: The New Robust Adaptive Median Filter (RAMF) is a novel filtering technique that aims to reduce noise in images, particularly in the presence of highly corrupted or noisy pixels. This filtering algorithm employs an adaptive approach where the median is calculated in a processing window, but without considering the noisy pixels during the initial computation. Keywords: Adaptive Median Filter (AMF); Weighted Median Filter (WMF); Noise Adaptive Fuzzy Switching Median (NAFSM) filter; Decision-based algorithm (DBA); Mean Square Error (MSE); Peak-Signal-to-Noise Ratio (PSNR) and Universal Quality Index (UQI)
A Blockchain-Based Distributed and Intelligent Clustering-Enabled Authentication Protocol for UAV Swarms

Unmanned aerial vehicles (UAVs) are operated remotely without the presence of a unified system of identity authentication, and wireless communications in untrusted environments can cause the loss of valuable data carried by UAVs. Traditional UAV authentication mechanisms are centralized approaches, which suffer from a single point of failure problem and may incur high complexity computations. Therefore, it is crucial to establish a distributed authentication mechanism between the ground station controller (GSC) and a UAV. Moreover, in case of UAV swarms, the high mobility of the UAVs affects the stability of UAV communications, which leads to the degradation of the UAV authentication performance. Addressing these challenges, we design a blockchain-based distributed authentication mechanism, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SwarmAuth</i> , for UAV swarms, where the GSC and UAVs follow a mutual authentication approach using physical unclonable functions (PUFs), and the K-means clustering-based intelligent approach is used to dynamically create location-based clusters. The blockchain helps store UAVs' authentication information in an immutable storage and the associated smart contracts provide a convenient access control model. The security analysis of SwarmAuth is carried out through both formal and informal proofs considering general attacks. Experimental evaluation shows that SwarmAuth can assure trustworthy communications and improve the network performance.
Assessing the Impact of Blockchain Technology on Internal Controls Within the COSO Framework

Developments in Blockchain technology and their increasing adoption have prompted examination of intersections with organizational internal control systems. Technological solutions leveraging blockchain reportedly enhance operational effectiveness and efficiency, core internal control objectives. Such improvements could increase financial and non-financial reporting reliability and facilitate regulatory compliance. The Committee of Sponsoring Organizations' (COSO) internal control framework provides a systematic methodology for designing and implementing effective controls utilizing blockchain technologies. Within the COSO 2013 framework's context, this study aims to identify risks that may arise from blockchain technology integration into financial reporting processes and outline corresponding control formulations. Specific focal points involve risk evaluation associated with blockchain technology adoption and control implementation proposals addressing identified issues. If properly designed, controls may optimize blockchain technology capabilities for transparent, accountable, and sustainable value generation. This initial examination offers strategic guidance on evidence-based advancement of business-community symbiosis locally and globally through continuous policy evolution. Regional contextualization and adaptability to emerging complexities will determine durability of theoretical edifices established.
A Review Study on the Impact of Online Collaborative Learning on EFL Students’ Writing Skills

This review study endeavors to review the literature on the impact of online collaborative learning on EFL students' writing skills. It also adds to the ongoing discussion about the way in which online collaboration might support writing and the extent to which online collaborative learning can facilitate mutual scaffolding among EFL students. In addition, it views the students’ perception and understanding of online collaboration as factors that need to be investigated to explore the impact of such an approach. The study utilized a qualitative method, which particularly relies on gathering materials and conclusions from existing literature such as books, papers, and articles. Based on the existing literature, online collaborative learning had a beneficial impact on the students' writing performance. The findings indicated that the implementation of online collaborative learning effectively facilitates the instructional and educational endeavors of both educators and learners. The observed results indicated significantly greater levels of accomplishment in comparison to those who did not use the same. This is achieved via the provision of feedback, explanations, and suggestions related to students' errors, as well as by promoting more opportunities for participation and collaboration. The evident advantage of online collaborative learning becomes clear when considering the impact of technology on improving students' writing abilities and knowledge. In a general sense, online collaborative learning may be seen as a potential substitute for traditional methods in teaching writing skills to English as a Foreign Language (EFL) or English as a Second Language (ESL) students.
Task-specific parameter decoupling for class incremental learning

Class incremental learning (CIL) enables deep networks to progressively learn new tasks while remembering previously learned knowledge. A popular design for CIL involves applying a shared feature extractor to learn old and new classes. However, this design can lead to representation interference, a phenomenon in which knowledge acquired from different tasks interferes with each other. This limits the ability to maintain feature information from previous tasks, especially without access to training data from previous tasks. To overcome this limitation, we present a novel CIL approach called task-specific model parameter decoupling, which includes a parameter decoupling (PD) framework and a dynamic-parameter-fusion (DPF) strategy. The PD framework compresses the knowledge of each task into a compact set of task-specific model parameters. In this situation, interactions between compact model parameters related to different tasks are eliminated to reduce representation interference. In addition, we adopt a DPF strategy to adaptively fuse the parameters of the larger model. As the learning task progresses, the DPF strategy enhances the model's adaptability and stability. Extensive experiments on benchmark datasets, including CIFAR100 and TinyImageNet, demonstrate the performance improvement of our approach over state-of-the-art methods.