Reviewerly/exHarmony huggingface dataset:
Integrated 3R Waste Processing Facility in Batan Indah as an Educational Waste Tourism Center

Abstract The management of waste has emerged as a pressing global concern due to its profound environmental and societal consequences. This paper presents the design proposal for an integrated waste processing facility, named Batan Indah, which serves as both a solution to waste management challenges and an educational center for waste awareness and education. The facility aims to exemplify the principles of Reduce, Reuse, and Recycle (3R) while offering visitors an immersive learning experience. Architecturally, the facility blends harmoniously with its natural surroundings, showcasing a strong connection to the environment. Abundant natural lighting and ventilation minimize dependence on artificial energy sources. The design proposal for the integrated waste processing facility in Batan Indah presents an innovative approach to waste management, emphasizing its educational aspect. By combining sustainable architectural design, immersive learning experiences, and community engagement, the Batan Indah facility seeks to promote the principles of the 3R concept and inspire visitors to actively participate in waste reduction, reuse, and recycling initiatives.
RF Scene Generation for Dynamic Spectrum Testing

The RF environment is a harsh and highly dynamic environment that includes both congested and contested spectrum. Typical testing lifecycles include initial simulations, then laboratory testing in simulated noise environments with a few channel impairments, followed by outdoor testing in unpredictable RF environments under challenging unknown channel characteristics and unintended interference sources. The large leap from predictable laboratory testing to unpredictable Over-the-Air (OTA) testing makes it difficult to evaluate and quantify the actual performance. The use of emulated RF environments or RF scenes provides greater insight into system performance in challenging, and dynamic environments, while also enabling agile iterative performance improvements to fine tune the system. This paper describes the creation of realistic RF scenes to emulate RF environments that include a variety of narrowband and wideband signals, and random traffic patterns. We will also describe a RF learning approach that learns the spectral and temporal characteristics directly from actual RF environments, enabling the creation of RF scenes that mimic the actual RF environment enabling cost-effective repeatable test scenarios with the same complexity and rich expressiveness of actual operational RF environments. RF learning also enables the cost-effective generation of the massive RF data sets required to train, validate, and test new innovative RF Machine Learning (ML) algorithms.
An Effective Approach to Evaluating Electronic Warfare Systems

Dominating the electromagnetic spectrum is a vital part of modern military operations. Electronic warfare (EW) plays a crucial role in this by denying an adversary's use of the spectrum for target location and intelligence gathering. There is a growing need for EW systems to jam transmissions more consistently, with innovative techniques to successfully prevent or deceive the threat. However, modern radar systems can detect irregularities in programmed electronic attack (EA) techniques. Ensuring that EW systems can produce these techniques without exploitable irregularities demands thorough and accurate EW tests and evaluation methods. Existing methods for waveform evaluation of EW responses can be costly in both time and resources. Further, complex EA techniques require evaluation at a high resolution and fidelity that cannot be easily achieved with common laboratory tools due to tradeoffs between bandwidth and resolution. With high-performance hardware and advanced digital signal processing (DSP), engineers can digitize and process large bandwidths of congested and noisy environments. Implementation of these techniques will help secure electromagnetic dominance and save the lives of countless warfighters. This paper will discuss methods and applications of advanced waveform analysis that enable quick and accurate characterization of critical parameters of advanced EW systems with the needed resolution for proper evaluation.
Techniques to Maximize O-Level Cyber Security Protection

Cyber security threats continue to grow on a daily basis with attacks coming from a wide range of sources including phishing, spear phishing, malware, ransomware, exploiting cloud vulnerabilities, Botnet, state-sponsored attacks, as well as others. Of the aforementioned, state-sponsored attacks not only pose a significant threat to western financial, educational and healthcare institutions, but also threaten the systems and data utilized by a wide range of government agencies and contractors. The military test community is not immune to these threats either with many currently deployed automatic test equipment (ATE) implementations, across the various maintenance levels from the flightline to the depot, being increasingly vulnerable. A growing number of Organizational Level (O-Level) test sets are now exposed to cyber threats as their architectures and interfaces continue to evolve to meet the ever-growing need for increased processing power and measurement performance. Further, O-Level test sets typically connect to the platform (i.e. aircraft) and as such, pose a much greater cyber-security threat. Physical threats have always existed, even for previous generations of test sets, but the nature of the vulnerabilities has largely shifted to new types of interfaces. Replacing legacy test sets, that were deployed with limited external connectivity and data storage capabilities, with advanced processor-based implementations exposes them to a new set of cyber security concerns including possible vulnerabilities with the operating system, the test development environment, the test executive (sequencer) as well as threats from other new interfaces that could possibly be exploited. This paper will delve into these and other possible vulnerabilities with modern O-Level test set implementations, including the impact from the underlying hardware and software architectures. It will discuss the Department of Defense (DOD) Security Technical Implementation Guide (STIG) for devices and systems and identify where the STIG requirements apply to O-Level test sets. The analysis will include aspects such as file protections, access control, data encryption, activity logs, user group accounts and privileges, test program set (TPS) event logging and others. The analysis will also cover cyber security benefits from utilizing a cyber-approved integrated test development and test executive environment, and identify techniques to maximize the proper protections for current and future deployments.
The digital reconstruction of rockery landscape based on NeRF

Rockeries, one of the key components of traditional Chinese gardens, are crucial to both landscape design and the preservation of cultural heritage. However, because of the rockery's intricate geometry, varied surface roughness, and wide range of colors, it can be challenging to precisely rebuild the rockery and preserve these characteristics. To investigate the data set of such a vast setting as a rockery, we created a dataset of the rockery landscape. It is challenging to achieve delicate effects with the conventional visual-based reconstruction method when reconstructing a rockery with intricate details and complex shapes. Therefore, Neural Radiance Fields(NeRF) method was introduced to reconstruct the rockery landscape. A new view synthesis method for rockery scene is proposed in this paper. NeRF is a new model that can represent a scene as a neural radiation field. Excellent view synthesis capability to generate high quality new rockery views. We collected and preprocessed the fabricated rockery dataset and trained the NeRF model using an improved Radam optimizer. The experimental results show that the method achieves good reconstruction effect in the rockery scene. This has played a positive role in promoting research on landscape architecture and the inheritance of traditional culture.
Synthesizing 3D VR Sketch Using Generative Adversarial Neural Network

Virtual Reality (VR) has gained significant attention in recent years as an immersive technology that enables users to interact with computer-generated environments. One essential component of VR experiences is the availability of 3D content, which can be time-consuming and labor-intensive to create. In this paper, we propose a novel approach for synthesizing 3D VR sketches using Generative Adversarial Neural Networks (GANs). By leveraging the power of GANs, our method allows for the automatic generation of high-quality creative 3D VR sketches, thereby reducing the burden on content creators and exploring the possibilities of VR content creation using Artificial Intelligence (AI) technologies.
Heterogeneous-training: A Semi-supervised Text Classification Method

With the advent of the information age, there are more and more text data on the Internet. As the most widely distributed information carrier with the largest amount of data, it is particularly important to use text classification technology to organize and manage massive data scientifically. In this paper, a semi-supervised ensemble learning algorithm Heterogeneous-training is proposed and applied to the field of text classification. Based on the Tri-training algorithm, the Heterogeneous-training algorithm improves the traditional Tri-training algorithm by using different classifiers, dynamically updating the probability threshold and adaptively editing data. A large number of experiments show that our method always outperforms Tri-training algorithm in text classification on benchmark text data sets.
LatentCluster: Clustering GANs Latent Space for Massive Conditional Sampling

Generative Adversarial Network (GANs) is a machine learning model implemented by neural networks traditionally employed for image sampling and capable of generating photorealistic images. However, the GAN standard training, which involves image synthesis through random input vectors, does not provide explicit control of the desired features in the output image. As a solution to this generation controlability issue, we propose a method named LatentCluster, which is based on the clustering of the latent space and is capable of sampling multiple random images with features preset by a user. In this way, it we expand the possibilities of applying GANs in tasks that depend on the generation of images conditioned to attributes, allowing the achievement of data that are more suitable for a given application. Additionally, the LatentCluster is architecture-agnostic and applied to unconditional and pre-trained GANs, without compromising the performance of the models that use it.
Research on Real-time Early Warning Mechanism of Aviation Safety Based on Finite State Machine Underlying in QAR Stream Data

Flight safety plays a crucial role in the civil aviation industry. However, contemporary research predominantly focuses on accident detection, and traditional early warning methods rely on crew members, rendering them less reliable. This study addresses these limitations by employing data mining techniques on Quick Access Recorder (QAR) stream outputs. The overarching strategy involves predicting future states based on current states. To achieve this, the paper constructs a state queue founded on the principles of Finite State Machines (FSM) to ascertain the current aircraft state from the QAR stream. Subsequently, features are extracted from the queue and utilized to predict imminent over-limit events. The efficacy of the warning mechanism is then validated using the Random Forest model. The real-time early warning mechanism proposed in this study offers an effective safety assurance for airliners without imposing undue computational power demands.
Enhanced feature extraction-based semantic segmentation network for remote sensing image using modified Swin Transformer

Remote sensing image segmentation is a specialized form of semantic segmentation that presents unique challenges not typically found in general semantic segmentation tasks. The key issues addressed in this study are the highly imbalanced foreground-background distribution and the presence of multiple small objects intertwined in complex backgrounds. However, existing methods heavily rely on convolutional neural networks (CNNs), which, due to their local nature, struggle to effectively capture global context. by the powerful global modeling capability of the Swin Transformer [1], this paper proposes a novel U-shaped network for remote sensing image semantic segmentation called Light Swin Transformer_Unet. In this network, the attention calculation of the Swin Transformer is modified and employed in the encoding part of the network. Additionally, an adaptive multi-level feature pyramid pooling based on CNNs is integrated into the auxiliary decoder of the Unet, creating a novel parallel connection structure with feature processing capabilities. This module effectively addresses the limitations of Transformers in focusing on local features. Experimental results on the Loveda [2] dataset demonstrate that the proposed network outperforms pure CNNs, pure Transformer networks, as well as networks that fuse CNNs and Transformers in other forms. Moreover, the proposed network achieves a slight performance improvement with a decrease in parameter count compared to the Transformer alone.The research findings provide a reference for the fusion network of CNN and Transformer, and offer valuable methods and techniques to address challenges in this field.
A Fast Self-supervised Dual Correlation Window based Stereo Matching

We propose a real-time dual-window-based dense Stereo Correspondence method. Although there have been a large number of related research in decades, Compared with deep learning and global optimization algorithms, correlation window based methods still have the advantages of speed, low memory requirements, and high robustness. The challenge in window-based method is to choose a matching window size that maintains detail and objects’ boundaries at the same time coverage of areas with low or repeated textures. In the face of this trade-off, the classical method prefers to choose a larger window in order to achieve higher robustness.
Pedestrian recognition method based on Jetson nano

This paper presents a novel pedestrian recognition method based on Jetson Nano, leveraging its advantages as an edge computing device with low latency and high privacy. Traditional methods for pedestrian recognition often require substantial computing resources and a strong network connection, making them unsuitable for edge devices. Thus, this paper proposes an edge computing method specifically designed for Jetson Nano, utilizing CUDA and TensorRT to achieve higher performance. This method utilizes a convolutional neural network to extract image features, which are then activated through Reshape, Transpose, and Sigmoid functions. The Slice operation is employed to extract objects within areas with relatively high probabilities. The position of the object is then adjusted using mathematical operations such as Mul, Sub, Add, Pow, etc., resulting in more accurate detection results. This paper demonstrates the effectiveness of using Jetson Nano as an edge computing device for pedestrian recognition and the potential of CUDA and TensorRT for optimizing performance on edge devices. The proposed method has broad applications in various fields, such as surveillance systems, autonomous vehicles, and robotics, where real-time pedestrian recognition tasks are essential.
The Traditional Cultural Interactive Experience System Based on Leap Motion

Chinese traditional culture has a history and customs of thousands of years, and one of the most significant things about these ancient cultures is to continue and disseminate them. In recent years, with the development of technology, more and more modern technologies have been used and integrated with traditional Chinese culture. This article elaborates on an interactive teaching tool created based on the Unity game engine and motion sensing technology Leap Motion, which allows students to participate in learning traditional Chinese culture.This paper is divided into 5 parts. The first part is an introduction to the project, mainly elaborating on the problems in current traditional cultural education and the contribution of this article to this issue. The second part mainly explains the dissemination tools of traditional culture and related research on Leap Motion. The third part mainly discusses the use of device Leap Motion and the methods for our team members to integrate Leap Motion into Unity scenarios. The fourth part is the specific operations in the scene. The fifth part is a conclusion.
Aspect-based sentiment analysis method using text generation

Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect terms, corresponding sentiment polarities, and opinion terms. Most studies only focus on a subset of these tasks, which leads to various complicated ABSA models. We propose a text-based generation framework for various ABSA tasks. It uses a sequence-to-sequence text generation method that can selectively output aspect terms, sentiment polarity, and corresponding opinion items of input sentences, than normalize the results using glove word vectors. Extensive experiments show the effectiveness of our proposed approach,achieves state-of-the-art performance on multiple datasets.
Lightweight Garbage Detection Algorithm Based on Improved YOLOv5s

Garbage classification not only helps to avoid resource wastage and environmental pollution but also reduces the cost of waste management, which is of significant importance for environmental protection. In order to promote the development of garbage classification, a high-precision garbage detection model that can be deployed on embedded devices is highly necessary. However, there are several problems with garbage detection models currently deployed on embedded devices, such as large algorithm parameters, large model sizes, and high computational complexity. To address these issues, a lightweight detection algorithm, Distilled_YOLOv5s_ABCS, was proposed by combining YOLOv5s and MobileNetV3. To compensate for the detection accuracy loss caused by the lightweight design, we use the coordinate attention module to replace the squeeze-and-excitation module in MobileNetV3 and add it to the head network. In addition, the integrated module of self-attention and convolution and spatial pyramid pooling fast module is added to the backbone network to improve feature extraction capabilities. The neck layer uses a weighted bidirectional feature pyramid network to enhance feature fusion. Finally, knowledge distillation is used to improve detection accuracy further. Experimental results show that compared to the original YOLOv5s algorithm, the Distilled_YOLOv5s_ABCS algorithm reduces the algorithm parameter quantity, computational complexity, and model size by 50.8%, 62.4%, and 50%, respectively, while slightly decreasing mean accuracy precision. Additionally, the image processing speed on the CPU increased by 21.3%. The mAP value of the Distilled_YOLOv5s_ABCS algorithm is 89.6%, which meets the requirements for deployment on embedded devices.
An Attention-based Audio-visual Fusion Method for Short Video Classification

The short multimedia video has become one of the most representative products in the new media era. Because short videos are highly time-sensitive, it is essential to manage and classify them efficiently. Currently, the primary approaches for short video classification are to extract the image features and then make judgments to complete the classification. However, audio information, crucial for classification tasks, is often discarded or used separately. To this end, we propose an attention-based audio-visual fusion method for short video classification. In this method, the attention module calculates the magnitude of the influence of image and audio information on the classification result, and the image and audio information are fused for the short video classification. The experimental results on different short multimedia video datasets demonstrate that the proposed attention-based audio-visual fusion method is effective and can significantly improve the classification accuracy of short videos.
Patient Information Retrieval Based on BERT Variants and Clinical Texts in Electronic Medical Records

Information retrieval is a task related to search a database for the most relevant similar objects to a given query object. In medicine, patient information retrieval is important to get the patients that are the most similar to a patient being considered. The resulting data can be further used for physicians to produce adaptive treatment plans as well as for other applications such as disease classification, re-admission prediction, and stay-length prediction. Due to its significance, several traditional information retrieval approaches were applied on medical databases. However, there is still a growing need for an effective solution to patient information retrieval in the context where more and more electronic medical records and their clinical texts are captured. In this paper, we focus on this task and propose to perform local learning on the BERT-based embeddings from clinical texts of patients to achieve an effective solution. The advanced properties of BERT variants help better represent each patient using the clinical texts instead of other data types like medication codes and demographic data. The experimental results on MIMIC III database have confirmed the effectiveness of our proposed solution. Above all, the better differences between our solution and the others in F-measure are statistically significant in all the cases.
Diffusion model for breast cancer segmentation

Breast cancer has become one of the leading causes of death among women, and in recent years, the incidence of breast cancer in China has been steadily increasing. However, breast cancer is a chronic disease with no obvious early symptoms, and once abnormalities are discovered, they are often in the middle or late stages. Clinical data shows that early detection and treatment can greatly improve the survival rate of patients. Breast cancer ultrasound imaging technology has gradually become the mainstream method of breast cancer diagnosis due to its low cost and non-invasive safety features, and the emergence of computer-aided diagnosis systems can better assist doctors in reading and diagnosing images, reducing misdiagnosis and missed diagnosis rates. Based on this, in order to improve the accuracy of existing breast cancer instance segmentation methods, we proposes a diffusion-model-based breast cancer instance segmentation method, Diff-bcSeg, which defines breast cancer instance segmentation as a denoising process from noise to filters. The trained model can reverse the noisy ground truth without any inductive bias from RPN, using randomly generated filters as input and output masks for one or more denoising steps. The proposed method is validated on the Breast Ultrasound Images (BUSI) dataset, and experimental results demonstrate that Diff-bcSeg has better detection and segmentation performance for breast cancer instance segmentation compared to current segmentation models.