Reviewerly/exHarmony huggingface dataset:
Adaptive Boundary Reference-based Dual-Images Reversible Data Hiding Scheme

In this paper, a novel dual-images reversible data hiding scheme is proposed. To provide competitive hiding capacity while maintaining acceptable image quality and lightweight computation complexity. Adaptive boundary reference is introduced. Experimental results confirm that the distortion of image quality is reduced when the payload is increasing. The average PSNR remains around 60 dB when the hiding capacity is up to 20,000 bits. Moreover, the computation complexity is less and it makes our proposed dual-images reversible data hiding more suitable for telemedicine scenario.
Effects of Haptic and Visual Senses on Angle Perception for Networked Virtual Environments

In this paper, we examine effects of haptic and visual senses on human angle perception for networked virtual environments by QoE (Quality of Experience) assessment. Each subject can touch angles in a 3D virtual space through a haptic interface device as well as watching them. By making a comparison among three cases, we clarify each effect of the two senses. One case uses only haptic sense, another case employs only visual sense, and the other case utilizes both senses. As a result, we can quantitatively confirm that the visual sense can differentiate angles more easily than the haptic sense.
Part-Double-Head Model for Smart Retail Products Detection

Retailers introduce self-checkout to increase checkout efficiency and improve checkout experiences. However, self-checkout nowadays still has multiple shortcomings. For example, it often requires an expensive installation cost. In addition, a self-checkout system based on computer vision (CV) still cannot overcome the occlusion problem and the cropping problem. To solve this problem, this study proposes a Part-Double-Head (PDH) method to achieve a low cost and efficient system. Through CV, deep learning (DL), and the PDH model. We solve the problem of commodities being occluded and cropped. The experimental result shows that the method can efficiently solve above the problem and can increase the mean average precision (mAP) rate by 99.46%.
A Study on Parameters of Piecewise Logistic Map over Large Integers and Processing Time

It is known that the piecewise logistic map proposed by Wang, Liu, and Lei has a wider range of the control parameter than one by the logistic map for generating pseudorandom sequences. We also realize that a sequence with long period is ob-tained by the large arithmetic precision. However, conditions for generating good random numbers at large arithmetic precision are not clear. In addition, we do not know a method for deciding a suitable initial value. For that reasons, we will use a large number obtained by some continuous mapping from a small integer. In this paper, we take account of the time to processing of multiple precision arithmetic, and we will offer suitable parameters for generating good random numbers by the piecewise logistic map.
Image Confusion Applied to Industrial Defect Detection System

There have been many related security issues about Artificial Intelligence (AI) in recent years. During the manufacturing process, products are captured by images for defect detection. If attackers use the model inversion attack to attack the AI model, the input image can be roughly restored, resulting in product information leakage. In this paper, we propose a system that confuses input images and uses them to train the model. Experiments show that our model has a high accuracy of 94.4% in defect image classification. Thus, the proposed system can achieve product information protection and accurate defect detection.
Two-way Connection between Virtual and Real Worlds: Real-time Synchronization Using VRChat

The interaction between the virtual world and the real world has received continuous attention in recent years, and this type of interaction has often been found unidirectional or asynchronous. By reviewing the literature, this study explained our view of the virtual world, as well as the necessity of media and the directional connection in the interaction of the two worlds. To validate our design concept, we created a wall in the virtual world that can be instantly synchronized and interact with the monitor in the real world. Based on our findings in the design process, we also proposed applying the concept to more senses in the future.
A Data Hiding Scheme Based On Absolute Moment Block Truncation Coding and Lookup Table

This research proposes a data hiding scheme which uses absolute moment block truncation coding (AMBTC) for image compression, and generates a reference lookup table which has a one-to-many relationship between the hidden data and the candidate bitmaps of the compressed image. When embedding data, the candidate blocks that match the hidden data are filtered through mean square error (MSE) function to select the optimal block. From the preliminary experiments, we prove that the proposed method is superior to other data hiding method in terms of data hiding ability and image visual quality.
SFG-Net: an Efficient Image to Image Translation Approach for Synthetic Fog Generation

In this study, we propose a new synthetic fog generation network (SFG-Net), which employs the two-stream image-to-image translation model as a base backbone and integrates a self-attention (SA) module for generating foggy images. With the presence of the SA module between the style pipeline and generator of the proposed SFG-Net, the style structure from style features of the input images is captured to advance the image translation performance. Experimental results show the effectiveness of the proposed SFG-Net in both quantitative evaluations and perceptual quality compared with the competitive method.
A survey of 5G network systems: trends and Deep Learning approaches

The use of artificial intelligence in technology has become increasingly common in different technology fields, being impulsed by a blast of use of deep learning models. Its use in mobile networks is on the rise and ranges from troubleshooting and error detection to the improvement and refinement of new technologies. This paper aims to point out the main Deep Learning techniques applied to 5G Networks, their main challenges and possibilities for future research, based on the analysis of articles collected from two public databases.
Low Parameter and FLOPs<sup>1</sup> Speech Densely Connected Convolutional Networks for Keyword Spotting

Due to the fast-paced nature of technology, people focus on keyword spotting technology for the use of human-computer interaction (HCI). In this paper, a keyword spotting technique based on the Convolutional neural network (CNN) method is proposed. This network model is modified with the densely connected convolutional network (DenseNet) and uses grouped convolution and deep separable convolution to construct complete keyword spotting tasks. Besides, we change the width and depth of the network to construct a compact variation of the network. We established the network using the Google Speech Command Dataset V2. Compared to different networks, our proposed network sacrifices a small quantity of precision to have a low number of parameters and floating-point operations (FLOPs).
Image Blending-assisted Few-Shot Cross-Domain Similarity Learning and Adaptation Tasks for Ambiguous Hazardous Incidents

Nowadays, machine learning technology is growing exponentially, our research integrates AI and digital twin and/or transformation implemented in a wide range of industries. The issues can be seen in the fields which is the use of knowledge from both the virtual and physical world adapted all the way through. Part of obvious issue seems similar to deep transfer learning, such a domain shift occurrence during two domains. Hence, our proposed framework was developed to learn domain-invariant representation through Kernel Higher-order Tensor Matching (KHoM) and emphasized by cross-domain similarity learning via SoftTriple. Results, where are evaluated on public dataset and new fatal circumstance data, have been investigated that our framework is able to diminish discrepant domains from transferable on higher-level feature domain-invariant lightly on a less exemplary adaptations, but be obtained tremendously by the backing of the recognizability and realizing of object homogeneity through learning the likeness.
Comparison of Statistical Algorithms and Deep Learning for Slovak Document Classification

This paper compares the statistical classification methods with deep learning methods for the document classification for documents in the Slovak language. The experiments evaluate the classification accuracy and times of execution. This study can help with selecting a natural language processing system for the Slovak language for devices with constrained memory and computational capabilities, especially cell phones. The classification algorithms are not constrained to Slovak languages. Adaptation of the algorithms to other languages requires only small adjustments.
A Study on Reaction-Based Attack on QC-MDPC McEliece PKC

Strong cryptography is essential for secure consumer electronic devices. In this paper, we evaluate the effectiveness of the reaction-based attack (RBA) on QC-MDPC McEliece PKC that uses a modified bit-flipping (BF) decoding algorithm where flipping bits are selected using the distribution of the numbers of unsatisfied parity-checks. We also examine a method to detect the RBA on QC-MDPC McEliece PKC. Simulation results show that the RBA is effective for the modified BF decoding algorithm, and also show that the RBA can be detected by the proposed method.
An Extension of High-Capacity Reversible Data Hiding in Encrypted Images with Bit-Plane Partition

In this paper, we propose an efficient extension of a previous reversible data hiding method in encrypted images that uses bit-plane partition. A main feature of our method is an extended self-embedding process. We introduce a more precise algorithm for prediction and a more effective embedding procedure for the self embedding. Experimental results show the effectiveness of the proposed method in terms of hiding capacity and marked image quality.
SPINDLE: Self-Pretrainable In-situ Normalizer for Deep Learning Error Function

For new datasets with distinct distributions from that of pretrained features, not only the model weights but also the activation function shapes should be fine-tuned for accelerating convergence, optimizing successive quantization and promoting cross-validation accuracy. In this paper, we propose a spindle-like ReLU-based radial basis error function called SPINDLE for self-pretrainable in-situ normalizer for deep leaning. Both off-line and on-line algorithms are also proposed for efficient training. Finally the proposed SPINDLE can save the logistic in a normalizing activation function, and the square-exponential operations in radial basis functions.
Research on the Usability of Hospital Website Interface

The usability of the interface reflects the quality of the hospital website. A good interface can make people feel the pleasure of being served by medical services when they contact the hospital website. However, the hospital website interface is often criticised. As a preliminary stage to explore the usability of the hospital website interface, this research proposes future development suggestions for the subsequent construction of the hospital website through the operation of five tasks and subjective evaluation of interface functions. Studies have shown that a good interface should achieve the above four aspects: concise and intuitive screen presentation, reasonable location placement, orderly information blocks, and smooth operation routes.
Design Methodology of Queue-Based Fast Classification for Sequential Minimal Optimization in SVM ML-Training

In this paper, we propose a design methodology of queue-based fast classification for sequential minimal optimization (SMO) in support vector machine (SVM) training. The queue is designed to tremendously reduce the searching space of weightings. Our method is useful to simplify operating steps of SMO and almost achieve the same performance in terms of classification accuracy with respect to full-search approach. In the Matlab simulation, our method is completely verified with 6 representative data sets. As compared to full-search and heuristic approaches, the running speed of our method is increased by 7.53 and 2.91 times, respectively. It features high efficiency without sacrificing classification accuracy.
Improved Hierarchical M-Net+ for Blind Image Denoising

Image denoising is a long standing ill-posed prob-lem. Recently, the convolution neural networks (CNNs) gradually stand in the spotlight and almost dominated the computer vision field and had achieved impressive results in different levels of vision tasks. One of famous hierarchical CNN-backbones is the U-Net which shows awesome performance in both denoising and other areas of computer vision. However, the hierarchical architecture usually suffers from the loss of spatial information due to the repeated sampling. It seriously affects the denoising performance especially the element-wise task like denoising. In this paper, we proposed an improved hierarchical backbone: M-Net+ for image denoising to ameliorate the loss of spatial details. Furthermore, we test on two synthetic Gaussian noise datasets to demonstrate the competitive result of our model.
Improved Small Object Detection for Road Driving based on YOLO-R

With the popularization of self-driving cars, more and more researches have been done on road object detection. However, many challenges remain to be resolved, such as the detection accuracy of small objects in the long distance. Therefore, we propose an algorithm based on YOLO-R to improve the detection accuracy to deal with the actual situation in this field. First, we set some conditions and propose some methods to balance the problem of extremely unbalanced size among each target label. Secondly, the Mish activation function is selected for training. Finally, we use the stochastic gradient descent (SGD) method to ensure that the best global solution can be obtained, and experiments on the BDD100k dataset show that our method has better results than other models in this dataset.
A Synthetic Approach to Validate the Usability of Anomaly Detection Model

In recent years, air compressors for industrial use have played a vital role in a vast number of industrial applications. The ability to pressurize air has been a boon to productivity at factories. Use of the air compressor under unexpected situations for a long time will cause serious damage to the air compressor and disrupt the manufacturing process. Therefore, anomaly detection is critical in air compressor maintenance, one of the challenges for many researchers using machine learning to prognostic and health maintenance air compressors. In this paper, we propose excellent machine learning for anomaly detection in an air compressor. We utilize a one-class support vector machine(OCSVM) to deal with anomaly detection in time series data and unsupervised learning. To design the experiment, we employed two datasets: one from a Taiwanese company with no abnormal data and no domain expert for labeling, and another from the FEMTO-ST (PHM IEEE 2012) Institute with domain experts for labeling. To demonstrate the promising performance, we used the PHM dataset to design experiments on both synthetic and measured data.
Supplementary Physical Device for In-Depth Augmented Reality Touring of Architectural Heritage Sites

Augmented reality (AR) that can overlay the interactive digital contents on real scenes has advanced the breadth and depth of the guided tour for architectural heritages. However, it increases the demand for precision in identifying real scenes without physical AR target installation for flexible and natural exploration. In this study, a customized physical convertible stand for a tablet computer that captures an extendable physical target object was developed to calibrate captured scenes and on-site viewing. The developed stand ensures an improved AR touring experience in receiving complex knowledge regarding visited cultural heritage sites.
Hotel Anti-Epidemic Management System Based on UV LED Disinfection

During the period of COVID-19, it is still inevitable for people to contact with each other based on the consideration of economic development. Therefore, the health management measures for staying in the hotel have become an important part of epidemic prevention. As we all know, ultraviolet (UV) light is an effective disinfection and sterilization method, which has been widely used in many applications. In this paper, a hotel anti-epidemic management system is proposed to disinfect the used rooms by using UV LEDs through WiFi communication with the front desk computer, and therefore it can protect quests from virus infection.
Pedestrian Intent Prediction by Dense Graph Transformer

In this work, we develop a Deep Neural Network (DNN) to predict pedestrian intent in a road where two input streams of subject images, and their skeletal joint points, bounding box points and bottom middle point of a scene are provided. The proposed DNN includes 3D convolutional neural network, self-attention layer, reduction layer for processing subject images, and dense graph convolution or dense graph transformer for skeleton-position data. The proposed DNN was verified on Joint Attention for Autonomous Driving (JAAD) database and achieved an accuracy of 82.34%. As compared to the conventional work [4], the proposed DNN reveals superior performance on the task of pedestrian intent prediction.
Image Inpainting in Frequency Domain with Wavelet Convolution

This paper shows that Time-Frequency Analysis (TFA) techniques for signal processing can be used on tasks of computer vision. The idea is as follows: we can build a simple network architecture like convolutional neural network (CNN), analyze hidden features by wavelet transform or other methods, and catch them into filters for weights by convolutions, transformers or other ways. It looks like we need to build the network with 2 stages to accomplish the idea. However, we actually can directly use TFA skills in one-stage network by some technique. Networks which build from this way not only has good performance, but also cost lower computing resources. In this paper, we mainly use wavelet transform on CNN to solve free-form image inpainting problems. And it shows our CNN model can nicely work in frequency domain.
Understanding Buyers' Behaviors in Vertical E-Commerce with Limited-edition Merchandise

Personalized recommendation is one of the keys to provide a successful e-commerce user experience. To offer such recommendations, the use of historical data and product metadata is critical. However, recently emerging vertical e-commerce that focuses on a specific product category faces challenges in making personalized user experience due to rapid product life cycle and few historical data. This study aims to understand user behavior in this new challenging vertical e-commerce environment with a real-world vertical e-commerce platform called Twenty. We analyzed Twenty's three-month order data. The results showed that generally buyers are not following popular sellers and have their own preferences.
VM Migration Considering Downtime for Accuracy Improvement in Multi-stage Information Processing System

In this paper, we propose a VM migration method that improves the accuracy in edge nodes of multi-stage information processing system even when the VM migration downtime is large. The previous method only performs well with short VM migration times. In contrast to previous studies, we place VMs to make a large contribution to improving accuracy by considering the system load degree and the characteristics of the tasks generated by each VM. The evaluation results show that the proposed method improves the accuracy by about 68% compared to the conventional method when the number of VMs is 25.
A Convex Optimization Method for Edge Signal Denoising over Graph

This paper presents a convex optimization method to de-noise the edge signal over graph which is a representation of network data. For this network, the interested signal is located on the edge domain rather than on the vertex domain where conventional graph signal processing algorithms are usually developed. Two main features of the proposed method are described below. First, to increase flexibility of de-noising method, equality constraint of flow conservation is relaxed to an inequality constraint with a prescribed upper bound. Second, to ensure that the de-noised signal is close to the measurement signal, the Lp-norm of error is minimized such that error norm can be adjusted to improve performance. Experimental results show hat the proposed method can provide smaller de-noised signal errors than conventional edge-Laplacian low-pass filter if parameters are suitably chosen.
Human Fall Prediction Using Ensemble Learning Technique

Accidental falling in the indoor environment during locomotion is very common among aged fellows. This can result in serious injuries or even death if not detected or treated on time. In this work, we applied different supervised machine learning techniques to predict fall or syncope, trained a physiological dataset, and compared their performances with respect to different parameters. Our study shows that ensemble technique gives the best results among the implemented models.
2DAN-BNN: Two-Dimensional AN-Code Decoders for Binarized Neural Networks

Binarized neural network (BNN) becomes the low-cost high-accelerated inference machine for consumer electron-ics. But for some safety-critical applications, reliability is more critical than cost and acceleration. AN codes are effective and efficient for improving the reliability of a BNN, but each class needs a decoder. In this paper, we propose a two-dimensional er-ror-locating technique for sharing only a decoder. From experi-mental results, the logic elements can be highly reduced up to 93%.
Data Augmentation Method for Improving Vehicle Detection and Recognition Performance

Vehicle detection and recognition are now implemented through powerful machine learning methods, those methods are not only relying on clever learning strategies, but also require high quality datasets. To obtain high quality datasets, time-consuming and grueling processing is needed. In this research, we proposed a data augmentation concept that is able to prepare high quality datasets for vehicle detection and recognition training in a more efficient approach. The effectiveness of our proposed data augmentation concept has been proved by applying it on the training data preparing process of YOLOv4 model. The result shows that the mean average precision (mAP) had increased 1.93% comparing to the YOLOv4 model which was trained without data augmentation.
Local Frequency Table for Context-based Adaptive Arithmetic Coding in Text Compression

Adaptive arithmetic coding is widely adopted in text compression. However, to achieve a high coding gain, an advanced way for context assignment is required. In this paper, the local frequency table technique is adopted to improve the coding efficiency of context-based adaptive arithmetic coding in text compression. With it, the probability distribution of the characters to be encoded can be estimated more accurately and a higher compression ratio can be achieved. Experimental results show that the proposed coding algorithm with a local frequency table outperforms other methods and achieves better compression performance for text compression.
A Novel Mouth-Controlled Mouse for Quadriplegic Patient

According to the special physical condition of quadriplegic patients due to high cervical spinal injury, we designed a new type of mouth-controlled input device. It uses the front teeth biting pressure and time perception method to operate corresponding three mouse functions within one second, without the need to blow or recite complex instructions, simple and intuitive. It can greatly improve the patient's computer control flexibility and input speed.