Reviewerly/exHarmony huggingface dataset:
VLSI Design of Number Theoretic Transform for BGV Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) can directly perform desired operations on ciphertext, but the efficiency of various FHE systems is highly dependent on how effectively the homomorphic multiplication is implemented. The Number Theoretic Transform (NTT) is a widely used method for carrying out large-sized multiplication. This work aims at developing efficient hardware design of Bluestein’s NTT for the latest BGV-FHE. Primes with low Hamming weight are selected to optimize the Barrett modular multiplier design and memory requirements. The experimental results demonstrate a significant improvement in the area requirement compared to the related work.
Application of Artificial Intelligence in Developing a Nursing Robot System: Preliminary design

Regarding existing technology, people with disabilities tend to be monitored using video devices for occurrences of falls or dangerous situations. In addition, health monitoring devices such as blood pressure monitors and blood glucose meters can be set to enable automatic data transmission to medical staff and family members. Many older adults hesitate to allow monitoring equipment in areas such as bedrooms, bathrooms, and toilets, where they tend to fall. Moreover, requiring older adults to wear devices involves potential problems such as forgetting or unwillingness to wear them. This study involves to develop and manufacture a care robot featuring an artificial intelligence behavior model that evaluates emotional feedback. Within this paper we will complete the construction of the robot, which involves the use of a multi-sensor data fusion technique for achieving obstacle avoidance and allowing the robot to operate autonomously in dynamic environments and daily life scenarios. Moreover, the robot is equipped with a vision and voice recognition module to collect information on care receivers’ expressions, behaviors, and voices, thereby establishing a data storage and management platform.
Stripe-Based Airlight Estimation Algorithm for Low Complexity Image Dehazing

Image dehazing is a post processing techniques to remove the haze in an input hazy image. Since the haze removal poses a challenging problem. In this paper, a stripe-based airlight estimation (SBAE) algorithm is proposed for low complexity image dehazing. At first, SBAE transforms RGB of a hazy image to its HSV. The depth information of each pixel can be estimated from HSV. Every maximum depth of each 5x5 patch in four stripes is collected. The airlight is estimated as an average of their corresponding pixel intensities in a hazy image.Experiment results show that the computation saving ratio (CSR) is 99% with a minor error rate of only 1.55%. SBAE greatly reduces the computation burdens while keeping the accuracy of arilight estimation. SBAE provides the forlow complexity image dehazing.
Enhancing Incremental Learning of CNN model using Filtered New Added Samples Assisted with XAI

In the incremental learning technique, the accuracy of the model is improved by increasing the number of training samples. However, there are some problems when making this training mode. First, it is not known whether the newly added training samples will affect the learning effect of the model. Second, we know the result of the model identification, but we don't know what features are used to identify this object. In this paper, we propose a training dataset curation approach to improve model accuracy with incremental learning. To select the most appropriate explainable AI (XAI) method, we use the intersection over union (IOU) metric to measure the range calculation of real objects and model features to know the rationality of the model explanation. As we know that increase suitable training samples can improve the accuracy of the model, we added the XAI method to filter out new samples that are not suitable for the training set. We use the crack identification dataset in our method, and the accuracy can be improved from 0.895 to 0.93.
A Consideration of Parameters for a Nonlinear Filter Generator and its Linear Complexity Profile

The authors explore the conditions of parameters in a Continuously Decimalized Maximum length (CDM) sequence to realize its uniform distribution bit patterns and evaluate the specific characteristics of CDM-sequences with the parameters.
A Fine-Grained Scheduling Based on GPU Sharing for Deep Learning Jobs on the Cloud

Based on the suspend and resume mechanism, this paper proposes fine-grained scheduling with a fragmented resource-reusing approach to improve the utilization of a GPU sharing system. The HRRN and the best-fit schemes are also adopted to alleviate the starvation problem for large-scale machine learning jobs submitted to the cloud. Experiments are simulated based on the trace logs and benchmarking data collected from a real cluster and the TF-Slim tool. Various image classification models are applied to the experiments to simulate heterogeneous job submissions to the GPU sharing system. Experimental results show that our approach improves GPU resource utilization by around 4.3 times and reduces overall makespan by at least 3.9 times compared to the sequential execution without GPU sharing.
The object detection in remote sensing imagery based on mosaic improvement

For real-time image recognition, YOLOv4 is currently the most widely used object detection algorithm. However, while the technology is advancing, the problem of improving misjudgment must also be faced at the same time. This study will conduct experiments on remote sensing images, and discuss the effect of data augmentation methods that can improve the amount of data and model performance applied to remote sensing images. Mosaic is a data augmentation method proposed by YOLOv4. Mosaic mainly stitches pictures together so that the model can learn multiple pictures at once and improve training performance. However, because of the way the pictures are synthesized, the interaction between the object and the background makes Mosaic unable to fully play its role probably. Therefore, this study will also explore the different effects of subsequent training and detection caused by differences in stitching methods, and how objects in remote sensing images may not be successfully identified, and propose a data augmentation method to solve the above issues.
Design of QR Code Tracking Car based on Monocular Vision

This paper designs an automatic tracking car that uses machine vision technology to realize QR code recognition. The car uses the MSP430F5529 chip as the main controller. It uses the OpenMV camera as the image acquisition sensor and the OpenCV library to process the acquired images. It can locate the target of the QR code by monoculture visual positioning technology. The car can obtain the distance between it and the leading vehicle, and compare the actual distance with the expected distance. The main controller can control the motor operation according to the distance deviation obtained, aiming to realize the automatic following function of the car.
A Study of Learning Perception towards VR Technology

VR can be used for creating situated learning environment, so most of research focused on applying VR on teaching and learning. Nevertheless, learning new information technology, including VR, is the educational target in senior high school. To engage the learners in learning information technology is an important educational task. Therefore, this study developed a systematic learning modular for VR technology based on learning by doing strategy, conducted a one-semester learning experiment in a senior high school, and investigated the learners’ perception towards VR technology learning. The results revealed that the learners showed high motivation and perception towards VR technology learning. However, some of learning dilemmas should be reduced in the future.
Single Underwater Image Restoration via Unsupervised Generative Adversarial Network and Contrastive Learning

Underwater image restoration has gained more and more attention recently due to its several applications in marine environmental surveillance-related tasks. In this paper, a novel unsupervised GAN (generative adversarial network)-based deep learning framework for single underwater image restoration is proposed. Without needing paired training images, we introduce contrastive learning with feature and style reconstruction loss functions in our unsupervised GAN-based structure to learn an image generator for translating underwater images to the corresponding in-air images. Extensive experiments have shown that the proposed method outperforms (or is comparable with) the state-of-the-art deep learning-based methods relying on paired/unpaired training data quantitatively and qualitatively.
Deep Violin: Deep Learning-Based Violin Bowing Training System

In this paper, a violin bowing gesture training system is proposed. Based on the positions of the skeletal joints of the hands of a user, we propose to use the long short-term memory (LSTM) the deep learning model for violin bowing gesture recognition. According to the hands API of MediaPipe, a user can use a built-in camera to learn the violin bowing gesture in practice. In the developed prototype, the overall violin bowing gesture recognition accuracy is 96%.
Quality Assessment of Image Retargeting based on Importance of Objects

Many novel image retargeting algorithms have been proposed to adjust the size of images to suit different display devices while minimizing perceptual distortion. Assessing the quality of retargeted images has become an important task for developing such schemes. In this study, we propose an image retargeting quality assessment method based on the importance of objects in an image. We utilize semantic segmentation to classify pixels and assign them different importance values representing the sensitivity of human eyes to distortion. A visual saliency map is created to better match the subjective perception of humans and is then used in the "Aspect Ratio Similarity" measurement to improve its accuracy. Since human eyes tend to be more sensitive to the information loss in images without prominent foreground objects, we introduce an information loss adjustment strategy for such images. The experimental results demonstrate that the proposed method is effective in evaluating image retargeting algorithms and outperforms existing quality assessment methods.
MDP: Privacy-Preserving GNN Based on Matrix Decomposition and Differential Privacy

In recent years, graph neural networks (GNN) have developed rapidly in various fields, but the high computational consumption of its model training often discourages some graph owners who want to train GNN models but lack computing power. Therefore, these data owners often cooperate with external calculators during the model training process, which will raise critical severe privacy concerns. Protecting private information in graph, however, is difficult due to the complex graph structure consisting of node features and edges. To solve this problem, we propose a new privacy-preserving GNN named MDP based on matrix decomposition and differential privacy (DP), which allows external calculators train GNN models without knowing the original data. Specifically, we first introduce the concept of topological secret sharing (TSS), and design a novel matrix decomposition method named eigenvalue selection (ES) according to TSS, which can preserve the message passing ability of adjacency matrix while hiding edge information. We evaluate the feasibility and performance of our model through extensive experiments, which demonstrates that MDP model achieves accuracy comparable to the original model, with practically affordable overhead.
Secure and Efficient Runtime Environment for Smart Contracts on JointCloud

Many cloud providers, including Amazon, Google, Microsoft, and Alibaba Cloud, offer support for blockchain cloud services that rely on a runtime environment, such as the Ethereum Virtual Machine (EVM), to execute smart contracts and ensure consistency between participants. However, existing runtime systems suffer from two main limitations. Firstly, traditional runtime systems like EVM cannot guarantee privacy protection as all the data uploaded to the blockchain is visible to all participants. This restricts the use of blockchain in limited scenarios. Secondly, each computation on the runtime system must be synchronized to all nodes in the network, resulting in a significant increase in computational overhead, which can be challenging to implement for more complex applications. One approach to address these limitations is to utilize Trusted Execution Environments (TEE) for blockchain runtime, which can provide privacy protection and mitigate redundant synchronization operations. However, using TEE for blockchain may significantly increase cloud costs. To overcome these challenges, this paper proposes PL-EVM, a new runtime environment for smart contracts that utilizes jointcloud. PL-EVM achieves high-security guarantees by using TEE to protect privacy-sensitive data and incorporates dynamic migration and splitting mechanisms to achieve high efficiency and low costs. Our evaluation results show that PL-EVM can improve performance and reduce costs by 4% to 32.22%.
A Vision Method for Step Detection Using Sensor Fusion of 2D LiDAR and Binocular Camera on Electric Mobility Scooter

In this work, we propose a method for road step detection via a 2D LiDAR and a binocular camera on the mobility scooter. Some visual experimental results can verify the correctness of our results.
Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms

In this paper, we propose a Graph Neural Network-based model to detect fraudulent users on e-commerce platforms without relying on rating scores. Utilizing user-product bipartite graphs and timestamp data, we capture temporal patterns and neighborhood information, creating a graph with multidimensional edge vectors. Our model demonstrates competitive performance compared to state-of-the-art methods, effectively identifying fraudulent users under data-insufficient conditions and enhancing the overall reliability of online platforms.
Bi-level Multi-Agent Actor-Critic Methods with ransformers

Recently, deep multi-agent reinforcement learning methods have witnessed great progress, including multi-agent actor-critic methods. However, it’s worth noticing there is a performance gap between multi-agent actor-critic methods and state-of-the-art value-based methods. In this paper, we investigate the causes and attribute inferior performance to issues of contribution-mismatch and indiscriminate guidance. To overcome these problems, we introduce a novel bi-level multi-agent actorcritic reinforcement learning approach with transformers, called BMT. Specifically, we propose a simple but efficient bi-level optimization mechanism to learn both global critic and agentspecific critic, thus jointly guiding the policy update. In addition, we adopt the transformer-based model as the policy network to decouple complicated relationships and generate flexible policy. BMT is also general enough to be plugged into any actor-critic multi-agent reinforcement learning approach, such as MAPPO, and equips it with strong expression. On multiple benchmarks including multi-agent particle environments and a challenging set of StarCraft II micromanagement tasks, large-scale empirical experiments demonstrate that BMT-based multi-agent reinforcement learning methods achieve superior performance over both state-of-the-art actor-critic and value-based approaches.
3D Dense U-Net-ASPP for MRI Imaging Segmentation of Breast Tumors

Breast cancer is one of the most common cancers among women in our country. Magnetic resonance imaging (MRI) has become a widely adopted medical imaging technology in the healthcare industry. It can help medical professionals accurately determine the size and scope of breast cancer. This study proposes a 3D Dense U-Net-ASPP model using a private dataset. The model addresses the issue of large data volume in image segmentation preprocessing and reduces the problem of feature loss by combining Atrous Spatial Pyramid Pooling (ASPP) and Attention Gate. Furthermore, the model’s performance is improved by adjusting the loss function through a combination of Focal Loss and Dice Loss.
Graph-Based Embedding Improvement Feature Distribution in Videos

Video understanding is a significant computer vision research subject since online video content is growing exponentially. Feature extraction and representation play a crucial role in video understanding tasks such as classification, segmentation, and recognition. However, the model’s learning is ambiguous since adjacent video frames typically have similar RGB features. To address this issue, we present graph-based embedding to enhance video feature distribution. We construct a graph-structured of videos by connecting similar features. Node embedding is generated by utilizing a graph model. Experiments demonstrate that our approach effectively improves feature distribution. The graph attention network (GAT) improves accuracy and editing score by 4% over the visual model.
FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.
Supporting a .csv-based Workflow in MongoDB for Data Analysts

The use of .csv files is very widespread, because of the simplicity of its tabular format and the support by popular editing tools. We propose a novel workflow for enhancing integration of such files with MongoDB storage, and investigate its applicability over a representative sample from the data. world collection. Compared to mongoimport (which is the MongoDB command-line file backup tool), our solution has much higher latency times, but automatizes the data type check and offers users two main degrees of flexibility, that are particularly useful in application development and deployment: possibility of spotting and rejecting duplicate records and possibility of rejecting single rows, instead of whole files in case of errors. Moreover, the reliance on the Measurify IoT application framework allows users to create application-relevant resources by simply enhancing .csv with semantics, while still providing a transparent end-to-end .csv file storage workflow.
Convolutional Neural Network Accelerator for Integrated Winograd and Convolution Computations

Due to the development of modern artificial intelligence, CNN has been widely used in many application domains. Because the architecture of CNNs has become more and more complicated in recent years, the amount of computation time increases dramatically. In order to meet the need of real-time computing, some research use the Winograd algorithm to reduce the number of multiplications. However, Winograd algorithm does not get computation benefit for all kernel size and stride. In this paper, we propose an integrated PE (processing element) architecture that can support both Winograd and traditional convolution computations. Winograd architecture is used for the most common 3x3 kernel with stride-1 sliding window calculation, and convolution operation is performed on the systolic array architecture for the other kernel size or non-stride-1 sliding window. Based on the proposed integrated Winograd and convolution PE architecture, we can reduce quite amount of the CNN computation cycles.
A Parallel Memory Defect Detection Method based on Sparse-Value-Flow Graph

Memory vulnerability detection aims to identify software defects that can compromise memory safety. However, existing methods often struggle to achieve both high precision and efficiency. This paper presents a high-precision memory vulnerability detection approach based on value flow analysis and parallel computing. We first construct a static semantic representation called SVFG to enable precise detection of memory vulnerabilities such as null pointer dereference and use-after-free. We then perform dependency-aware path feasibility analysis using an SMT solver to reduce false positives. Finally, we develop a task-level parallel framework to accelerate the constraint solving process and improve efficiency.We evaluate our approach on the Juliet test set of over 2,000 test cases and 7 open-source projects. Experimental results show that our dependency-aware analysis can achieve 0.5%-2.05% false positive rates, outperforming traditional approaches and existing tools. Our task-level parallel framework can achieve up to 3.25x speedup with 4 computing nodes.Our study demonstrates that combining value flow analysis and parallel computing is a promising way to enable highly precise and efficient detection of memory vulnerabilities. For future work, we plan to integrate pointer analysis to support more complex codes, and optimize the granularity of parallelism to improve scalability. Overall, this paper presents a static analysis based method to address the inherent trade-off between precision and efficiency in memory vulnerability detection.
Efficient Multi-Receptive Pooling YOLOv5 with Coordinate Attention Module for Object Detection on Drone

Object detection is the most basic and significant research in computer vision in images, and it is a study to discriminate the position and class of an object. This operation has been continuously researched for the past few years. Object detection performance based on accuracy is gradually improving due to the recent development of hardware such as GPU computing power and cameras. Object detection operations grafted in drones can be implemented in many domains. To perform object detection algorithms in real-time in drones, the applied network must be lightweight. For an algorithm capable of real-time operation on low-cost devices, this paper proposes Efficient Multi-Receptive Pooling YOLOv5 with Coordinate(CAM). Efficient Residual Bottleneck and Efficient Multi-Receptive Pooling make the model lighter by reducing the number of parameters, and the CAM improves the object detection rate of the model. The model is trained using the VisDrone dataset, and the mAP value increased by about 19% to 20.6 mAP, and the number of parameters decreased by about 6% to 1,663,599.