Reviewerly/exHarmony huggingface dataset:
High Availabale Hadoop Deployment Modes with Enterprise-Level capabilities

Businesses today recognize the tremendous value of data and seek to proactively leverage it to drive growth and success. Therefore, they adopt a data-driven approach, collecting, analyzing and using massive volumes of data from multiple sources. Data-driven companies need to invest in advanced technologies such as data and analytics platforms, to unlock the full potential of their data to gain significant competitive advantage.Accordingly, the technology innovations supported these business trends over the years by continuously releasing new platforms, patterns and architectures capable of scaling and managing large data volumes. Apache Hadoop emerges as one of these platforms offering distributed storage and processing running on physical commodity hardware and capable of scaling to support petabytes of data. However, this kind of platforms when deployed in an enterprise datacenter, are usually confronted with the growing usage of virtualization as a core component of infrastructure provisioning.This paper addresses infrastructure considerations of Hadoop deployment within enterprise's datacenters. It covers deployment scenarios on both physical and virtual servers with respect to prerequisites, limitations and constraints of each one of them. Furthermore, it suggests a hybrid approach with enterprise level capabilities usually required in datacenters. Finally, the suggested infrastructure choices are exposed and discussed with perspectives and future research areas.
Radio frequency fingerprint-based unmanned aerial vehicle detection method

With unmanned aerial vehicle (UAV) technology advancing at a rapid pace, the accurate monitoring and detection of UAVs have become increasingly crucial. Traditional methods relying on radar, vision, and optical tracking may fail to detect small-sized UAVs. This research proposes an architecture for unmanned aircraft signal radio frequency fingerprint (RFF) recognition based on Empirical Mode Decomposition (EMD). The architecture retains certain Intrinsic Mode Functions (IMFs) to describe the RFF of the signal, utilizing deep learning techniques to extract fingerprint features and perform signal recognition. Validation of the proposed architecture was conducted using ADS-B signals. Experimental results demonstrate that, compared to other traditional machine learning recognition methods, the proposed architecture achieves higher recognition accuracy in both high and low signal-to-noise ratio environments. This study provides the strong technical backbone for the potential development of ever more robust and advanced UAV detection systems, potentially with far-reaching implications for ensuring UAV aeronautical safety and detection of the electromagnetic environment.
DialCL-Bias: A Semantic and Contextual Framework to Identify Social Bias in Dialogue

The content generated by dialogue systems has been found to contain social bias due to the social bias in the corpus and the algorithm design. We propose a Dialogue-based Contrastive Learning for identifying social Bias (DialCL-Bias) framework in dialogue based on two dimensions-relevance and fine-grain. For the identification of social bias relevance, we propose a Dialogue-based Semantic Contrastive Learning (DialSCL) method. DialSCL converts simple dichotomy into triple input and decomposes the classification target based on data annotation rules for achieving semantic-level classification. We use a Key Words and Cosine Similarity (KWCS) method to construct difficult samples and achieve improvement in DialCCL. For the identification of fine-grained social bias, we propose a Dialogue-based Context Contrastive Learning (DialCCL) method. DialCCL simultaneously learns the context-level features of samples and the parameters of classifiers in the same space to naturally generate a classifier. Finally, we measure the degree of bias in the open-domain dialogue corpus and the responses of ChatGPT by using the social bias identification classifiers.
Contrastive-Aware ViT for Weakly Supervised Semantic Segmentation

Class activation maps (CAMs) are crucial in weakly-supervised semantic segmentation (WSSS) tasks. However, challenges arise when the initial CAM quality is limited, leading to diminished performance during the refinement and post-processing stages. While Vision Transformers (ViTs) enhance initial CAMs using self-attention mechanisms and class tokens, they fail to leverage additional class-wise and patch-wise information. In this paper, we propose a contrastive learning approach to effectively utilize this information and generate superior initial CAMs. Our Contrastive-Aware ViT framework encompasses Patch-to-Patch (PtP) intra-image contrast, aligning patch representations within an image, and Inter-Class Image (IIC) contrast, aligning class-wise predictions across a batch of images. Evaluating on the PASCAL VOC 2012 dataset, our method achieves notable improvements of 1.4% and 1.6% over the MCTformer baseline in the train and val datasets, respectively. Ablation studies on PtP and IIC further demonstrate the superiority of our method across multiple diverse object cases, highlighting its effectiveness in WSSS tasks.
Research on MacBERT-Based Multi-Type Questions Extractive Machine Reading Comprehension

In this paper, we proposed a multi-layer pre-training approach to overcoming the limitations of extractive machine reading comprehension (EMRC) in capturing global semantic information in depth and facilitating deep interaction between text and questions. The proposed framework adopted the Masked-Language Model correction BERT (MacBERT) model and multi-layer perception (MLP) to predict the probability of each location as an answer. To address span-extractive, unanswerable and YES/NO questions, we employed Bi-directional long short-term memory (BiLSTM) and self-attention to create different targeted layers of the neural network model. The novel model exhibits robust generalization and interactive extraction capability. With the Chinese Judicial Reading Comprehension (CJRC) dataset, the experimental results show that the proposed algorithm brings significant performance improvement by 3.5% in civil cases and 4.9% in criminal cases in terms of F1 score.
Learning with Local Gradients at the Edge

To enable domain adaptation of AI on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD). tpSGD uses layer-wise stochastic gradient descent (SGD) and local targets generated via random projections of the labels to train the network layer-by-layer with only forward passes. It doesn't require retaining gradients during optimization, thus greatly reducing memory allocation compared to SGD backpropagation (BP) methods. Compared to other target projection methods, tpSGD generalizes the concept to arbitrary local layer-wise loss functions. Our method performs comparably to BP gradient-descent within ~5% accuracy on relatively shallow networks of fully connected layers, convolutional layers, transformers, and recurrent layers. tpSGD also outperforms other state-of-the-art gradient-free algorithms with competitive accuracy and less memory and compute time.
Research on High Altitude Intelligent Network Monitoring and Video Enhancement Algorithms for Electric Power Construction

The construction of overhead transmission lines has widely applied technologies such as intelligent equipment, high-precision sensing, and wireless ad hoc networks, achieving intelligent visualization of centralized control and solving the problem of “blind traction” in tension wire laying construction. However, there are issues with low integration and high installation difficulty in engineering applications. Therefore, a high-altitude integrated network monitoring platform has been designed to conduct finite element stress analysis on key components, and trial production and testing have been conducted. In low illumination environments such as cloudy days and object occlusion, the images captured by imaging equipment at the construction site of power transmission and transformation projects often have problems such as low brightness and contrast, severe loss of detail information, and a large amount of noise. This type of image not only interferes with people’s judgment ability, but also hinders the machine vision system from extracting key features of the image. Through low light intensity enhancement, the quality of images can be effectively improved, resulting in richer texture details, which is of great significance and application value. Therefore, in order to better improve the visual effect of the image, this project has designed and implemented an effective low light image enhancement method. Firstly, the illumination of each pixel is estimated separately by finding the maximum value in the R, G, and B channels. By adding a structure before the initial illumination image, the initial illumination image is refined to serve as the final illumination image. By constructing a good illumination image, corresponding enhancement can be achieved. This study provides strong support for the safe construction of power grid infrastructure projects.
Target detection based on improved YOLOv7-tiny

As deep learning technologies continually advance, enhanced performance metrics are being realized in the realm of object detection. To address the challenge of ship detection in optically captured imagery set against intricate terrestrial and maritime backdrops, this study introduces a refined detection paradigm founded on YOLOv7-tiny. Initially, the backbone network juncture of the baseline model is supplanted by the streamlined MobileNetV3 architecture. Subsequently, a weighted Bi-directional Feature Pyramid Network (BiFPN) is integrated at the feature amalgamation stage to facilitate cross-scale coherence and weighted attribute fusion. Lastly, anchor frames optimization is achieved via the K-Means technique, and the CIOU loss function is supplanted by the EIOU loss. Evaluated on the Seaships (7000) dataset, the augmented model exhibits a detection precision of 98.3%, a enhancement of 0.2% relative to its predecessor. Concurrently, the computational and parametric overheads are curtailed to 6.9GFLOPs and 4.18M, representing reductions of 47% and 31%, respectively. Thus, the optimized YOLOv7-tiny framework offers a computationally efficient yet highly accurate solution for ship detection tasks.
Research on Cloud Computing Task Scheduling Based on Improved Particle Swarm Optimization Algorithm

As Internet applications and big data technology rapidly advanc, the cloud computing industry is booming. Users can now obtain the required resources from the cloud computing environment according to their personal needs. The objective of this paper is to reduce the time required for task completion. Based on the characteristics of particle swarm optimization algorithm (PSO) and the characteristics of cloud computing task scheduling problem, an improved PSO algorithm is proposed to address the challenge of scheduling cloud computing tasks. To begin with, this paper analyzes the development status of cloud computing, and deeply studies the concept, characteristics and goals of task scheduling in cloud computing. Afterwards, the theoretical underpinnings of the PSO algorithm are delved into, and its distinguishing characteristics are fully explored: it has the advantages of fast convergence and simple calculation, but it may fall into local optimal solution. In this paper, a new inertia weight optimization scheme utilizing a sine-based approach is introduced. After analyzing the characteristics of cloud computing task scheduling, the PSO algorithm is discretized for task scheduling. The simulation results show that the improved discrete particle swarm scheduling algorithm (IDPSO) is superior to the standard discrete PSO algorithm, which reduces the time of task scheduling and improves the scheduling efficiency.
Exposing Computer-Generated Images Via Amplified Texture Differences Learning

Many Computer-Generated (CG) images are spreading widely on the Internet, which may deliberately misinform or deceive the public. Therefore, distinguishing CG images from natural photographic (PG) has become a frontier research topic in the field of image forensics. Although many algorithms have been proposed, it is still very challenging to detect CG images generated by the recent cutting-edge generative methods. Besides, most existing algorithms tend to generalize poorly when facing different unseen multimodal generative models. To address this issue, a novel method based on amplified texture differences learning is proposed to tackle this problem. We first design a deep texture enhancement module for discriminative texture amplification. Specifically, a semantic segmentation module is utilized to generate semantic segmentation map for the affine transformation operation guidance, which can be further used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and enhanced images are fed into a hybrid neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. By verifying on several commonly used benchmark datasets and a newly constructed dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The benchmark is available at https://github.com/191578010/DSGCG. with more realistic and diverse images, the experimental results demonstrate that the proposed approach outperforms some existing methods.
Rate Control Optimization for Joint Geometry and Attribute Coding of LiDAR Point Clouds

With the increasing utilization of dynamically acquired Light Detection and Ranging (LiDAR) point cloud data, the significance of LiDAR point cloud compression has grown substantially. Bit rate control plays a pivotal role in LiDAR point cloud compression. This study introduces a novel bit rate control approach for LiDAR point cloud compression. By constructing rate and distortion models based on the geometry and the attribute of the LiDAR point cloud, as well as developing a comprehensive distortion model encompassing both geometry and attribute, this approach transforms the bit allocation problem for geometry and attribute into a constrained optimization problem. Consequently, it determines the optimal bit allocation for geometry and attribute. Furthermore, a rate control algorithm, incorporating parameter updates, is proposed for precise bit control. Experimental results demonstrate that our method achieves a remarkable overall BD-Rate performance, with an average bit error rate of merely 0.93%.
A Novel Learnable Orthogonal Wavelet Unit Neural Network with Perfection Reconstruction Constraint Relaxation for Image Classification

CNNs utilize lowpass frequency features, evidenced by max pooling operations that preserve dominant features. Addressing this, we used a one-layer FCN using both coarse and detailed components via DWT. We then developed UwU, a learnable wavelet-based unit that integrates the PR constraint relaxation, allowing feature map component fine-tuning. Distinctively, UwU’s coefficients are trainable, unlike prior studies. This is the first work that utilizes PR constraint relaxation to enhance CNNs. Our innovative techniques serve to enhance stride-convolution, pooling, and downsampling units in CNNs. We tested these improved units using the ResNet family architectures against traditional frequency and wavelet-based units. Performance metrics from CIFAR10, ImageNet1K, and the DTD show promising results. Particularly, while CIFAR10 results are on par with other methods, there’s a marked performance improvement on ImageNet1K and DTD. Further, integrating UwU into the ResNet18-based encoder of the CFLOW-AD system yields competitive anomaly detection results, particularly for hazelnut images in the MVTecAD dataset.
TDID: Transparent and Efficient Decentralized Identity Management with Blockchain

Decentralized identity (DID) is an identity management framework aiming to return the ownership of an identity to its corresponding user. Recent studies propose to store the identifiers of DID issuers and implement identity management systems based on blockchain. However, existing systems cannot avoid identity tampering and verifiable credential abuse of decentralized identities, which makes the identity management opaque. In this paper, we propose TDID, a Transparent and efficient Decentralized IDentity management system with blockchain. The key insight behind TDID is to manage the registration and authentication of DIDs via smart contracts, and design Structured Merkle Patricia Tree (SMPT) as an underlying data structure to store identity data on blockchain. The smart contract based processes can improve transparency of decentralized identity management, while the SMPT data structure can realize efficient storage of DID data. We implement and evaluate TDID on different identity management operations, and the experimental results show that TDID can achieve about 3.1 times for write operation and 6.3 times for read operation while improving the transparency of DID management.
Shoe-Type-Force-Feedback Device and Falling Sensation with Two-Step Dropping

In this study, a two-step dropping model was proposed to replicate a long-distance fall in a virtual reality (VR) space, while realizing only a short drop distance in reality. In a previous study, a two-step dropping model representing a falling motion was proposed and presented to humans in real space. This concept replicates the sensation of a long-distance fall by presenting the dropping motion to the wearer only at the start and landing of the fall in the VR space. A two-step dropping device was fabricated using an air cylinder. A two-step dropping motion was realized by combining the open/close control of the conduit with a directacting solenoid valve and pressure control with a proportional solenoid valve. In the sensitivity evaluation experiment, participants were presented with a combination of the device's operation and VR images. The evaluation scores for the sense of reality of falling in the two-step dropping condition tended to be higher than those in the other conditions, compared to the condition in which the device was not operated or only one-step dropping was presented. This confirmed that the two-step drop was an effective operation for experiencing a free-fall sensation.
DehazeDM: Image Dehazing via Patch Autoencoder Based on Diffusion Models

Image dehazing is a crucial computer vision application with the primary objective of estimating haze-free images from hazy images. Deep neural network architectures have emerged as the dominant approaches and achieved remarkable progress. However, due to the intricacy, existing dehazing methods need help to train large deep learning networks. This work proposes a novel image dehazing network based on Diffusion Model (DehazeDM). Firstly, by segmenting the image into patches during the sampling procedure, we can dehaze images of arbitrary size. Then we compress the image into the latent space via the auto-encoder model and conduct the diffusion operation in the latent space, significantly decreasing the computational complexity associated with the task while exhibiting negligible effects on the perceptual fidelity of the resultant images. Extensive experiments verify the effectiveness and the superior performance of DehazeDM in image dehazing.
Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation.
EdgeCPS-AI Knowledge Sharing Model for Supporting Computing Partition Services*

The rapid development of the AI industry had led to the widespread utilization of numerous edge devices in the real world. However, AI services still heavily rely on high-performance computing systems, which means that these devices are not being effectively utilized. To address these issues, EdgeCPS technology has emerged. EdgeCPS is a technology that supports the seamless execution of various services, including AI applications, through the interconnection of edge devices and edge servers, as well as resource and function augmentation. In order to appropriately utilize edge devices in an EdgeCPS environment, the paper proposes an EdgeCPS-AI knowledge sharing model for supporting computing partition services. The proposed EdgeCPS-AI knowledge is represented as a graph, which systematically structures AI service-related information that arises on virtual and physical edge devices. This knowledge provides not only partitioned AI weighted models in order to support the characteristics of EdgeCPS, but also enables flexible provision of various information required for AI services. Thus, this approach can facilitate the reconstruction of AI services according to service requirements and maximize the utilization of edge devices. To achieve this, a microservice-based computing partition AI service is devised and experimentally proven by constructing a Kubernetes system using 8 heterogeneous edge devices and a graph DB system consisting of 2,762 nodes. Experimental results show that performing computing partition services on edge devices is significantly more efficient in terms of overall resource consumption, with up to 85.57% improvement, as compared to performing the same tasks on high-performance computing systems.
Visual Tracking Based on Efficient Dual-Branch Siamese Network

In this paper, we propose a dual-branch Siamese network for visual object tracking. The proposed network consists of two distinct branches: a shallow network branch and a deep network branch. The shallow network branch focuses on precisely locating the target object and improving the anti-interference ability to similar objects, while the deep network branch focuses on capturing the more abstract semantic features of the object. Additionally, a multi-scale key feature fusion module is embedded into the shallow network, enabling the model to accurately locate the target object. Furthermore, we leverage the attention mechanism to further enhance the robustness of the model. Experimental results on three different public datasets demonstrate that our method outperforms state-of-the-art tracking algorithms.