Reviewerly/exHarmony huggingface dataset:
The Development of Blockchain Based Knowledge Management System Model at EPC Projects to Improve Project Time Performance

EPC project is one of complex construction project types which differs from ordinary construction projects. It owns more frequent delayed occurrence than ordinary construction projects. EPC projects tends to be labor and knowledge intensive. Inputs and outputs at EPC project management involve various knowledge and intellectual creative activities. Thus, the EPC project management process is a knowledge management process. Knowledge Management System (KMS) aims to improve the quality of human resources in an organization by improving communication among all parts of the organization and increasing knowledge mastery by transferring knowledge. The application of KMS with a blockchain-based system is expected to be a solution for the needs of distributed knowledge transfer, high-speed transfer of knowledge and information, fast access to knowledge and high information security. This study uses the Tiwana model framework, and for organizational knowledge mapping uses the Zack KM model cycle, while the SECI model is used for knowledge formation. The quality of the resulting blockchain-based KMS platform is tested by using expert validation. The research result shows that blockchain-based KMS model can be well implemented with excellent validation so it becomes a solution to increase knowledge and to improve project time performance in EPC-X project.
Classical and quantum computing methods for estimating loan-level risk distributions

Understanding the risk distribution for a single loan due to modelling and macroeconomic uncertainty could enable true loan-level pricing. To implement such analysis, rapid real-time methods are required. This article develops a Monte Carlo method for estimating the loss distribution on classical computers. For this analysis, multihorizon survival models incorporating the competing risks of default and pay-off were created on Freddie Mac data. The Monte Carlo simulation results fit well to a Lognormal distribution. Knowing in advance that a lognormal distribution is a reliable fit means that only tens of Monte Carlo simulations are needed to estimate tail risk in a single account forecast. We also explored the feasibility of using quantum computers that may be available in the near future to perform the calculations. Leveraging previous quantum algorithms for value-at-risk estimation, we show that a simplified version of the problem has potential for significant speed enhancement, but the full competing risks approach will require additional quantum algorithm development to be feasible.
Quantized Lite Convolutional Neural Network Hardware Accelerator Design with FPGA for Face Direction Recognition

In this study, a quantized lite convolutional neural network (CNN) is applied to accelerate the computations of face direction recognition with Xilinx ZedBoard FPGA platform. The utilized 8-layer lite CNN includes three convolution layers, one max-pooling layer, two average-pooling layers, two fully connected layers. Firstly, the weighting parameters and deviation values of each layer in the CNN are extracted by the training process with software, and the then quantization precision by 8-bit integer (INT8) is used as the inference calculations. By the quantized lite CNN model through the hardware acceleration, the facial direction is correctly detected to achieve fast recognition. Comparison with the software-based implementation in personal computer, the speed-up ratio by the FPGA-based acceleration is about 1.44 times with 0.289 seconds inference time.
Cross Domain Deep Learning for Noise Removal from LDCT Images

In this paper, an unsupervised cross domain deep learning framework for noise removal from LDCT (Low Dose Computed Tomography) images is proposed. To tackle with the problem that paired LDCT image training data for noisy images and the corresponding clean versions are rarely available in real scenarios, the presented model is trained in an unsupervised manner with unpaired training images. Relying on the auto-encoder architecture, our framework aims at learning two deep generators/decoders for producing clean and noisy images, respectively, by learning disentangling feature representations. In our generators, our deep residual blocks and the spatial and channel attention modules with dilated convolutions are used for learning multi-scale image features. To train our image generators, unsupervised adversarial domain adaption is used to learn robust and invariant image representations. For each unpaired training images, by feeding the encoded image content features from the noisy training image to the clean image generator, we aim at generating the corresponding clean image. On the other hand, by feeding the encoded features, fused by the encoded content features from the clean training image and the encoded noise features from the noisy training image, to the noisy image generator, we aim at synthesizing the noisy image. To ensure the stability of this cross domain transformation, the cycle consistency loss is also included to keep the backward transformation from the synthesized images to their respective original domains. Extensive experiments on LDCT image denoising have shown the feasibility of the proposed framework.
Smart Speaker Based on Detection of Millimeter Wave

In order to resolve privacy concerns in smart homes, we propose a gestures controllable smart speaker with a millimeter wave radar. Furthermore, the radar can be turned to face wherever nearby hand-clapping sound comes from. By running deep neural networks (DNN) on a small, powerful computer-NVIDIA Jetson Nano Developer Kit, we can analyze and classify 5 dynamic arm gestures in real-time. The results of gesture recognition will cause the corresponding function of controlling music. The average accuracy is above 99%.
Voronoi Diagram based Collision-free A* Algorithm for Mobile Vehicle in Complex Dynamic Environment

Path planning is the research focus of robot development and design. As technology advances, mobile vehicles for various jobs are used in different environment. Therefore, it is an important research to plan an optimal path that is able to avoid obstacles and consider time and distance to reach the goal. In this paper, to improve the efficiency of mobile vehicle path planning, the Voronoi diagram is used to describe the map, and the shortest path tree is built by Dijkstra algorithm. Re-planning path task is activated when moving obstacles approach the mobile vehicle. The collision-free A* algorithm that take account of the directions and positions of the moving obstacles will effectively avoid collisions and find optimal path according to the shortest path tree. In the simulation results, there are several random moving obstacles in the map to simulate the complex dynamic environment. The mobile vehicle could reach the goal without any collisions, and only add a small amount of time and distance.
An Automated Pricing System for a Buffet Restaurant with Daily Menu Updates in Health Village using Incremental Learning and Cloud Service

This study solves the problem of food assessment for the elder in a health village, via Nvidia Jetson devices and increasingly comprehensive transfer learning techniques. For the increasing of dish dataset, incremental learning is used. The main contributions include: first, building a food recognition system through transfer learning, incremental learning and edge computing; second, the ResNet-18 based model in the system has mAP 100% accuracy.
Combination of Augmented Reality with Chatbots for Visual Aids in Nutrition Education

Advance with times, people are paying more and more attention for healthy lifestyle. The food you eat in life is closely related to health. Therefore, dietary education has been promoted in Taiwan from elementary school campuses in order to propagate nutrition knowledge. Hoped that children and students can understand the ingredients, nutrition and source of food to develop healthy eating habits and to respect for the environment, nature and life. In this paper, we build a visual aided learning system by combining augmented reality (AR) and chat robot technologies. Students can use this proposed system to learn the ingredients and nutrition of food. It is able to automatically and intelligently identify the food and tell which of the six categories the food belongs to by using standard food guide pyramid. This way for informal and visual learn not only is interesting, but also achieves the purpose of dietary education.
Anomaly Detection in Airport based on Generative Adversarial Network for Intelligent Transportation System

Deep learning is used in various application, and there are many outstanding performances in many fields recently. Generative Adversarial Networks (GAN) is one of the deep learning models proposed based on the zero-sum game theory and has become a new research hotspot. One of the important issues in intelligent transportation system is airport anomaly detection. However, there is a crical problem that the abnormal data set is relatively insufficient, and even the normal data. The insufficient data set makes difficult to adopt deep learning technique for training a good model. Therefore, this paper adopts the Generative Adversarial Network to obtain data distribution through unsupervised learning and generate more information. In addition, GAN is less limited by the amount of data, so we use GAN-based model for implementation of anomaly detection in airport. The latent vector space of all nomal images is learned by GAN, and abnormal images can be distinguished through particular nerual network structure of GAN. The proposed method has significant performance as seen in the part of experiment.
On Multimodal Semantic Consistency Detection of News Articles with Image Caption Pairs

Recently multi-modal consistency detection has been proposed as method to combat disinformation. However, state-of-the-art methods lack the fine grained localized evidence needed for consistency detection. Current methods also struggle on longer texts that are present in news articles. We propose an ensemble method that combines a series of visual detectors and BERT based NLP models to compute consistency between modalities. The proposed method is effective in both detecting the standard image-caption pairs and news articles containing multiple paragraphs. Our method can localize and provide fined grained evidence towards its given responses. We evaluate our method on a MSCOCO image-caption subset and image & text inconsistency evaluation of news articles from the U.S. DARPA SemaFor program. We achieved an 83% AUC on the gathered MSCOCO dataset, which shows the effectiveness of our method.
An Energy-efficient and Accurate Object Detection Design for Mobile Applications

Deep Convolutional Neural Networks (DCNNs) are imperative to state-of-the-art computer vision algorithms. In spite of the attractive qualities of DCNN s, they have been excessively expensive to be applied on large scale high-resolution images and video sequences. In order to implement DCNN models on edge platforms, we tend to optimize the DCNN model by considering energy efficiency and detection accuracy simultaneously. In this paper, we analyze the energy consumption, detection accuracy, and execution speed of our model and those of the state-of-the-art models based on a mobile platform called Jetson Nano. We adopt the performance index from Low Power Computer Vision (LPCV) challenge which considers power, mAP and FPS at the same time to evaluate these models in an overall point of view. On Jetson Nano, the presented system boosted with the GoP-mode technique can achieve an execution speed of near 20 frames per second, and high mean average precision of 59.9% under MS COCO test sets. Compared with the state-of-the-art models, e.g., YOLOv5, the LPCV score improves as high as 76.33%. If the GoP-mode acceleration is included, the LPCV score of Agilev4 reaches even 90.6 times of that ofYOLOv5.
Wavelet Frequency Channel Attention on Remote Sensing Image Segmentation

In recent development of semantic segmentation, the deep convolutional encoder-decoder has become mainstream schemes for remote sensing images. In this paper, we proposed a U-Net like architecture for segmentation of remote sensing images using wavelet frequency channel attention (WFCA) blocks.
A New Ecosystem of Virtual Daily Life: Exploring the Intuitive Manipulation Based on the Transformation of Physical Experience Between Virtuality and Reality

This research has designed an interactive experience of step-by-step behavior content feedback through intuitive manipulation to propose another interactive interpretation of the new ecosystem. The research results hope viewers will no longer interact through non-intuitive methods but with the experience of body accumulated from experience to connect with digital content by internalizing themselves into the virtual world to enjoy the interactive feedback under intuitive control.
A Drone Human-Machine Interaction Method Based on Generative Adversarial Network

Commercially drone are mainly operated by dedicated personnel with trained skills. The high learning cost of operation will discourage some potential users. In this paper, we propose an human-machine Interaction to drone by intercepting the visual images of robots and use Generative Adversarial Network(GAN) to train. A camera is used to intercept the operator's gestures, and the photos with the operator's gestures are converted into control commands to improve the accuracy of operation in complex backgrounds. As a result of this research, drone flight control can be accomplished in more complex backgrounds, greatly simplifying operator stress.
Round Pill Shape Recognition System Based on AY Deep Learning Model

The appearance of pills includes various but similar round shapes, for example, round-flat, round-convex, ellipsoid and sphere. These round shapes will lead to be difficult recognized using traditional image recognition approaches. Therefore, an Attention-YOLO deep learning model (AY model) was proposed in this paper. The AY model completes the task of recognizing pill image, each image contains only a round pill. Compared with other deep learning models, the AY model can mostly focus on extracting the features of pills images to classify the appearance of different round pills. The results of experiment show that the accuracy rate of the AY model is 92.28%, which represents that the round pill recognition system can effectively assist medical professionals to identify round pill and improve medical quality.
Enhancing Recognition Accuracy of Urban Flooding by Generating Synthetic Samples Using CycleGAN

In traditional supervised learning technique, when the number of label samples is sufficient, the accuracy of the model will be high. But, some problems will happen for this kind of training model to be applied to outdoor environment. First, there are not stable and sufficient training samples for the target model. Second, the labeling cost of samples is always high for retraining a new model after a period of time. In this paper, we design a real-time urban flooding detection system based on CCTV images. In order to solve the problem of insufficient samples for special class such as flooding, we use CycleGAN to generate some synthetic samples for training the model. In order to get good samples from the generated samples, we propose a filtering approach to filter out some bad samples. We take 100 CCTVS in our experiments, and the results show we can enhance the accuracy to 97.2%.
DriverID: Driver Identity System Based on Voiceprint and Acoustic Sensing

The identification of drivers is essential for many applications, such as attribution of liability for car accidents and driving risk assessment. Most existing driver identification systems adopt identity keys (such as car keys and smart cards) or biometrics technology (such as face recognition, iris recognition, fingerprint recognition, voiceprint recognition, and vein recognition, etc.) to identify drivers. However, these schemes are unable to detect driver changes during a trip. In this paper, combining voiceprint and acoustic driving characteristics, the driver identity system DriverID is proposed to identify the person who is actually driving. DriverID uses the Deep Residual Network (ResNet) to construct an acoustic recognition model based on the voice key recorded by the driver. In addition, the Convolutional Neural Network (CNN) is used to construct an acoustic driving action recognition model based on the reflection of acoustic signals generated by the user. Combining the two recognition methods, DriverID can correctly identify the driver with high probability. It is believed that DriverID is a practical driver identity system.
A Hypergraph Convolutional Neural Network for Node Classification of Citation Network Data

This paper provides a hypergraph convolutional neural network (HGCNN) for node classification of irregular citation network data. First, hypergraph representation is used to describe the more general relation among the data, and the node classification problem is described. Then, the hypergraph convolutional operator and activation function are employed to construct a two-layer HGCNN model with sparse dropout regularization to transform the node feature vector to predicted label. Next, the cross entropy between true label and predicted label are chosen as the loss function to train the model weights and bias vector of HGCNN model. Finally, three citation network datasets are applied to demonstrate the effectiveness of the HGCNN model according to classification accuracy, learning curve and data visualization.
Research on User’s Subjective Preference of Taohuawu New Year Painting Based on CycleGAN

Objective. Exploring a feasible method for studying users’ subjective preferences of image samples based on style transfer; exploring contemporary young people’s subjective preference for different styles in the historical development of Taohuawu New Year painting. Methods. A detailed review of the development and style evolution of Taohuawu New Year painting. CycleGAN (Cycle Generative Adversarial Networks) was used to transfer the two artistic styles of Changmen Kusu Prints and Taohuawu woodblock New Year paintings to the typical representative pictures of Taohuawu New Year pictures, and to conduct user preference experiments on the images of the two styles. Results. The images of Changmen Kusu Prints style had higher user preference. Conclusions. CycleGAN can be effectively applied to the study of users’ subjective preference for images; Changmen Kusu Prints is more favored by contemporary young people in terms of quality, style and subject matter, and it is necessary to focus on its inheritance and development.
Gaussian correction for adversarial learning of boundaries

Social networking sites often monitor the response to brands, events and activities during personal chats or videos. Here, the facial expression of the speaker can be used for automatic ranking of products. However, manual classification of videos puts the identity of the speaker at risk. There is imminent danger of fake videos circulating that are generated using style transfer. In this paper, we target both these challenges by using an adversarial model that can segment a face from the background scenery and occlusions. The segmentation for a fake video will be of poor quality compared to a real video. Previous segmentation models could only be trained on a few objects and failed on scenic images with occlusions. Here we propose an image translator that learns the boundaries of objects during training using Gaussian correction. To determine the parameters of the Gaussian distribution we make use of a Lyapunov candidate function that converges to a global maximum. We apply the model to segmentation of faces and cars in photos. We also apply it to the task of style transfer to the background without affecting the foreground object. The proposed method outperforms baselines by over 20% on segmentation metrics such as IoU and BFScore.
Design of a Fuzzy Algorithm-Based Evaluation System for the Effectiveness of International Online Chinese Listening and Speaking Teaching

With the increasing popularity of online foreign language teaching and learning practice, learners and teachers have developed a high demand for evaluation of teaching effectiveness. When we focus discussion on international Chinese teaching, which has been developed for a relatively short time and not experienced enough, online teaching effectiveness evaluation has become an important obstacle to the development of teaching. This paper introduces a hybrid technique based on fuzzy evaluation method, for determining and suggesting possible types of errors in international Chinese online listening and speaking instruction and giving suggestions for improvement. The system can help learners to identify and determine the types of errors in Chinese listening and speaking learning in a timely manner and make a more objective and comprehensive evaluation of learning performance; at the same time, it helps teachers to trace the effectiveness of teaching design and implementation in a targeted manner and make corresponding scientific decisions. This hybrid technology combines existing language teaching evaluation models, takes advantage of data from online education, and creates corresponding criteria through machine learning fuzzy algorithms and large data sample training, combined with the theory of effective teaching evaluation, which is beneficial for all participants of online Chinese listening and speaking teaching to improve their learning effectiveness.
Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VIPR) has great potential for intelligent video surveillance systems at night, but it is challenging due to the huge modal gap between visible and infrared modalities. For that, this paper proposes a minimizing maximum feature space deviation (MMFSD) method for VIPR. First, this paper calculates visible and infrared feature centers of each identity. Second, this paper defines feature space deviations based on these feature centers to measure the modal gap between visible and infrared modalities. Third, this paper minimizes the maximum feature space deviation to significantly reduce the modal gap between visible and infrared modalities. Experimental results show the superiority of the proposed method, e.g., on the RegDB dataset, the rank-1 accuracy reaches 92.19%.