Reviewerly/exHarmony huggingface dataset:
Smart Touchless Control with Credit-Card Sized Radar Sensor and Microcomputer

A smart, low-cost, stand-alone, and reliable touchless human-computer interaction (HCI) using a tiny 60 GHz radar sensor and a small Raspberry Pi microcomputer with advanced radar signal processing and machine learning (ML), is proposed and implemented. Three feature extraction techniques and various ML algorithms are explored and compared. Smart gesture classification is realized by an ensemble ML model with 3 non-deep and deep learning (DL) algorithms and the majority voting scheme to improve classification accuracy. Both radar signal processing and ML algorithms are implemented in Python language in a Raspberry Pi microcomputer with an optimization by TensorFlow Lite, which reduces DL CPU time by 120 times. The proposed HCI is also integrated with a menu driven use case, which could be easily adapted to many other gesture control applications for home and industrial appliances.
Three Dimensional Microscope Image Super-resolution Using Deep Learning and Self-similarity

It has recently become possible to capture three-dimensional volumetric images of plant roots using a microscope. However, due to the limited time available to capture images of living plants’ roots, which are constantly changing, the depth resolution is low. This study aims to create depth-resolution images using super-resolution technology. Existing methods require a pair of high-and low-resolution images to train a model. However, such images are unavailable for the images which need to be developed into higher resolution. In this study, the self-similarity of plant images were used to solve this problem and a convolutional neural network to train on yx-plane images was proposed. The trained model was then used for yz-plane image super-resolution. The proposed method has good performance, according to the experimental results.
Embrace Imperfect Datasets: New Time Representation for RFF Identification

As the inherent attribute of equipment circuit hardware, Radio Frequency Fingerprints (RFFs) is hardly-forged and has become one of the most powerful guarantees of physical layer security. Most existing RFF-based methods ignore the temporal relation and are designed under an ideal dataset with a large number of samples and complete signal records, thus they tend to be less versatile in real-world scenarios. To address this problem, we propose a novel time representation method for wireless signal pictorialization called modified gramian angular fields (MGAF), which depicts the characteristics of the signal along the time axis through the transformation of coordinate system and a representation of trigonometric difference. After that, a channel-selectable convolution neural network (CNN) is used to extract high-dimensional feature vectors as the RFFs for further identification. The entire experiments are conducted with purposely poorly designed datasets. The results shows the accuracy can reach at 94.82% with only three half-sine waves and 99.26% with a quarter of the preamble at the SNR level of 30 dB.
PAVEMENT: Passing Vehicle Detection System with Autonomous Incremental Learning using Camera and Vibration Data

Systems that detect vehicles passing through roads play a significant role in ITS (Intelligence Transport Systems), due to their wide applicability to traffic monitoring and analysis for road construction/repair planning, congestion, and prediction. Among various systems using cameras, doppler sensors etc., a system that uses road vibration to detect passing vehicles is promising since it has advantages in terms of weather conditions and deployment/operation costs. However, it suffers from the human labor to prepare ground truth labels for training models. In this paper, we propose PAVEMENT, a novel Autonomous Incremental Learning based traffic-census sensor system using a piezoelectric vibration sensor and a video camera without human intervention. PAVEMENT consists of two models: the video-based model which detects vehicles by using bounding boxes (detected by YOLOv3 and DeepSORT) and the vibration-based model which uses road vibrations to detect passing vehicles. To reduce the burden of collecting ground truth labels, we apply linear discriminant analysis and incremental learning to train the vibration-based model by using the result of the video-based model as ground truth. Once the vibration-based model is trained, it can be used for traffic census on roads without the video camera for various conditions (weather, lighting, and other environmental factors). We collected the video and vibration data of more than 4,000 passing vehicles on roads in different places and applied our method to the data. As a result, PAVEMENT achieved over 98.4% accuracy and 98.0% f1-score in detecting passing vehicles using the model trained with 15 incremental learning steps in 1 minute interval.
Kernel Extreme Learning Machine-Based Dynamic Interval Construction for Outlier Detection of Telemetry Data

Due to the limited frequency spectrum resources, it is challenging to satisfy the Telemetry, Tracking, and Command (TT&C) requirements of mega-constellations in the near future. An intuitive idea is to compress the telemetry data with high redundancy, but outliers in telemetry data will significantly deteriorate the compression performance. Currently, outlier detection algorithms require a combination of expert experience and physical models, which have limitations in space environments, especially where unexpected failures undergo. To tackle these problems, this paper proposes an outlier detection method for telemetry data based on Kernel based Extreme Learning Machine (KELM). Additionally, to improve outlier detection accuracy, the Improved Sparrow Search Algorithm (ISSA), a novel swarm intelligence optimization technique, is adopted to optimize the algorithm parameters jointly. Take the actual telemetry data of the smart communication satellite of Tsinghua University as a case study to verify the superiority of the proposed algorithm. Also, the simulation results demonstrate that the proposed algorithm can efficiently improve the TT&C capacity of mega-constellations, even without additional ground TT&C stations and Tracking and Data RelaySatellites.
Extracting Associative Entities From Logic Rules in Knowledge Graph

The task of associative entities extraction is to provide top-ranked entities in knowledge graph (KG) w.r.t the query entity. In this paper, we propose the method for extracting associative entities from logic rules. We first use the AMIE+ algorithm to extract logic rules implied in KG, and then give the idea of resource allocation to calculate the association degree between entities. Experimental results show that the effectiveness of our proposed method outperforms other competitors.
Selective Use of Skeletal Information to Reduce Computational Complexity of Motion Matching

In order to improve sports skills, the comparison of the movement using the video is very effective for progress. Existing studies have been using posture similarity to map the motion timing. However, these methods have a disadvantage of a long execution time. Therefore, we propose a method to reduce the computational complexity by selectively using only data that is particularly effective among feature data used for mapping. Through evaluation experiments, we confirm that it is possible to reduce execution time without sacrificing performance even when the number of feature data is reduced. Based on the results of the evaluation experiment, we discuss the causes of the increase or decrease in accuracy for each motion and the factors that affect the accuracy of the mapping.
Measuring Human Perception of Audiovisual Errors using EEG

Audiovisual synchronization is an essential indicator of video quality. The degradation in the quality of experience caused by such synchronization errors is often measured using the mean opinion score (MOS). However, this method is susceptible to emotion and bias. Electroencephalography (EEG), as an objective tool, overcomes the drawbacks of subjective testing for evaluating human perception. In this work, we measure the human perception of audio and video synchronization based on the EEG approach through a series of experiments. A scoring model was developed for audio and video under multilevel synchronization errors by extracting the power spectral density (PSD) of EEG signals as features. The use of EEG signals for perceptual ability assessment of audiovisual distortion provides a potential neurally informed approach that is more objective than the high-level cognitive activity in subjective data.
Effectiveness of Machine Learning to Predict Machining Troubles

In the current cutting process, cutting tool wear always occurs during the cutting process, resulting in cutting problems such as chipping of cutting edges. Therefore, it is very important to prevent cutting problems before they occur. At present, however, skilled craftsmen judge wear by listening to the sound of the cutting process, which is very unstable. In this study, we focused on the effectiveness of anomaly detection by machine learning and considered that if we could detect anomalies in cutting tools by introducing a Support Vector Machine and Neural Network, it would lead to the prediction of cutting machining trouble. The purpose of this paper is to show that anomaly detection using a Support Vector Machine and three Neural Networks is effective in predicting cutting problems using sound data during cutting.
Expression-controllable Facial Video Generation for Impression Quantification

For realizing impression quantification of human behaviors, it is required to prepare various videos of human behaviors with slightly different head/eye motions and expressions and ask many participants to tell their impression of each of them. For this purpose, we propose a method for generating facial videos of various people with various expression from a single video of a certain person. The proposed method is based on First Order Motion Model (FOMM), which generates facial videos in real-time from a target image and a source video, but extended to enable to control the facial expression in a generated video by incorporating mechanism to control the degree of smile and gaze direction in FOMM. Besides, in order to save the effort on editing the facial expression on each frame, we also implement a function to interpolate the weight values for each frame from manually assigned weights just for sprase frames. We confirm that the proposed method generates natural facial videos with variously controlled expressions.
Performance Evaluation of Device LED Extraction for Data Center Infrastructure Monitoring

Availability is the core metric of a data center operation. To be available at any time, data centers must use a real-time and precise failure detection method. However, current methods are not enough to bring data centers to their goal. One of the practical device failure detection methods is visual monitoring. Human inspectors detect failures by checking LEDs of infrastructure devices. This manual method requires a lot of effort as well as it is not suitable for real-time monitoring for big scale data centers. The goal of this study is to achieve real-time and high accuracy failure monitoring for the data center. This paper presents performance evaluation of an automatic LED extraction system which is a part of work to achieve an automatic data center monitoring. The system performed the LED detection with an accuracy of 83.7%.
Multi-condition Training and System Combination for Automatic MOS Prediction

The quality of text-to-speech synthesis and voice conversion has been improved. To evaluate their quality, the mean opinion score (MOS) is widely used but its scoring requires human evaluation. Automatic MOS prediction can reduce development costs. The VoiceMOS challenge is a shared task for automatic MOS prediction in which the MOS of synthesized speech taken from the previous Blizzard challenges and voice conversion challenge is predicted. The challenge has main and out-of-domain (ood) tracks; we found that automatic MOS prediction systems show poor performance for the ood track. To improve this, we introduce multi-condition training. Experiments show that multi-condition training significantly improved the MOS prediction performance. Visualizing the embedded vectors of MOS prediction models shows that multi-condition training increased the margin between samples from two tracks.
Hardware Accelerator for Symmetric Permutation-Diffusion Image Encryption

Image encryption is crucial in many security-critical applications. Images have intrinsic features such as bulk data capacity and high redundancy encryption such that traditional text-based encryption methods are not suitable. Conversely, chaotic permutation-diffusion image ciphers receive a lot of attention. In this paper, we propose a new encryption algorithm based on the permutation-diffusion scheme. The algorithm is suitable for pipeline design. To achieve high throughput, a hardware accelerator with 3-stage pipeline is designed to realize the new proposed method. The design was successfully implemented and synthesized with TSMC 90 nm technology. Furthermore, the security analysis reports show that the proposed design has not only high performance but also strong resistance to various attacks.
M-DenseUNet: Multi Dense Encoder Connected UNet for Biomedical Image Segmentation

In biomedical image segmentation, the desired performance is necessary for more detailed segmentation. In this paper, we propose the M-DenseUNet which combines multi Dense Encoders and U-Net. The encoder of M-DenseUNet consists of convolutional layers, Dense Block and Transition. We show that such a network can strengthen feature propagation and encourage feature reuse to get details.
Deep Reinforcement Learning with Intervention Module for Autonomous Driving

Deep reinforcement learning (DRL) can be used to solve decision-making problems in changing and complex environments. It is widely envisioned to have great potential for enabling autonomous driving. However, the large and continuous state and action spaces of autonomous driving tasks in general lead to low exploration efficiency and significantly affect the model training speed. In this paper, we propose a novel autonomous driving framework based on DRL with an intervention module. The module utilizes historical information to predict the status of future states so that evaluation of potential actions can be inferred. By this means, an intrinsic reward is generated to prevent DRL agent from entering poor states with useless exploration. Integrating our DRL model with a perception module which utilizes supervised multi-task learning to extract useful features from raw sensor data, the proposed framework can notably improve the efficiency of DRL training. A case study on the open racing car simulator (TORCS), with inputs from both distance sensors and vision sensors, is carried out to demonstrate the effectiveness of our method.
The High-Efficiency Motion-Blurred Video Restoration Method by Feature Enhancement-Based Deep Learning Network

To achieve high-efficiency motion-blurred video restoration for hand-held cameras, this paper presents a feature enhancement-based deep learning network which mainly employs the channel attention layer to generate the most effective features for improving the image-deblurring effect. In addition, by weighting the color saturation of the input image of training process to preserve its original clarity, the inherent problem of turning the clear part of an input image into the fuzzy part, caused by the inappropriate convolution results in the previous video deblurring methods using deep learning networks, can be substantially reduced. Thus, if more feature information can be effectively extracted, the feature extraction process does not require much computational effort. Experimental results show that the proposed method has lower computational complexity and higher restoration quality, especially for the numbers and characters, compared to other deep learning network-based methods.
APPLICATION OF K-MEANS METHOD IN THE SPREAD OF POSITIVE CASES OF COVID-19 IN SALATIGA CITY

Covid-19 is an infectious disease caused by SARS-CoV-2. It was discovered in Wuhan, China. It has been spread in every countries, especially in Indonesia. It has spreading all districts/cities in every province, one of it is City of Salatiga. This research used to determine the grouping of the spread of Covid-19 positive cases in each "kelurahan" in order to find out which villages has contributed the most positive cases. The research uses the calculation process for the distribution of clusters, it is divided into 3 which are categorized as overage (from the range 200-150), moderate (from the range 100-55), and few (from the range 50-5). This grouping process is assisted by the RapidMiner Studio tools. From this calculation, cluster 1 (C1 = a lot) is the highest cluster consisting of 5 villages, cluster 2 (C2 moderate) is a medium cluster consisting of 6 villages, and cluster 0 (C3 is few) is a small cluster. consists of 12 villages. From this grouping, it can be seen that cluster 1 is the highest cluster because the highest range is in cluster 1. The results of this grouping can be applied and can provide information about the spread of positive cases of Covid-19 in Salatiga City.
AUTO SCALING DATABASE SERVICE WITH MICRO KUBERNETES CLUSTER

Data storage media, or what is often referred to as a database is something that is quite vital for technological developments. As the amount of data increases, it allows database services to experience downtime. For this reason, it is necessary to build an infrastructure that can replicate itself, so that it will avoid downtime. This infrastructure can be built using a container orchestration tool called Kubernetes which has high availability and autoscaler features, so it can replicate and guarantee service availability, to avoid downtime. This research builds a MongoDB NoSQL database service. This service is built using micro Kubernetes clusters from several different data centers. This service also implements a horizontal pod autoscaler feature that is capable of replicating pods, to increase high availability and avoid downtime. The autoscaling process will be tested by providing a load request for the service. Testing is done several times on each server. This study will compare the MongoDB service that was built monolithically with a micro Kubernetes cluster, and with HPA features and without HPA features by paying attention to several things. Based on Response Time, Response Code per Seconds, and CPU Usage, the results obtained are that the service built on a micro Kubernetes cluster with HPA features is the best, with a constant response time value below 100 ms, Response Code per Seconds reaches 500 threads per second. seconds, and CPU Usage in the range of 30 – 55%.
THE INTRODUCTION OF HIJAIYAH LETTERS IN SIGN LANGUAGES USING AUGMENTED REALITY TECHNOLOGY

The use of communication through sign language is generally carried out by someone who has a hearing barrier and communicates directly verbally, one of which is deaf and deaf. Deaf people have problems receiving information from the environment which results in less-than-optimal delivery of learning material so that it affects their learning achievement. One of them is the achievement in the field of Islam by recognizing hijaiyah letters. Therefore, we need a media that can optimize other senses, namely the sense of sight, one of which uses Augmented Reality technology. Making hijaiyah letter recognition learning applications using sign language with Augmented Reality technology which is expected to help deaf people in receiving material and teachers / teachers in delivering learning material. In this study, using the Multimedia Development Life Cycle research method, application testing uses the Alpha testing method with Black Box testing and Betta testing on user satisfaction. The test results 96% of respondents stated that the use of augmented reality technology helps in the learning process so that it can be a solution in tackling the problem of less than optimal acceptance of information from the environment so that it affects the learning achievement of people with speech deafness. Based on the test results 86.4% of users of the hijaiyah letter recognition application with the language used to assist in the learning process, especially the Koran lesson using the language of the manager.
Photon statistics and non-local properties of a two-qubit-field system in the excited negative binomial distribution

In this paper, a quantum scheme for a two-qubit system (2QS) and field initially prepared in the excited negative binomial distribution is presented. The field photon statistics is detected from the evolution of the Mandel parameter, while the evolution of von Neumann entropy detects the nonlocal correlation between the 2QS and radiation field. The concurrence is used to detect the qubit-qubit entanglement during the time evolution. The dynamical properties of single-qubit and two-qubit quantum Fisher information are investigated. We visualize the number of photon excitations on the field in negative binomial states with influence of photon success probability. A connection is provided between the dynamical behaviors of these statistical quantities. We have found that the proposed quantities are strongly influenced by the number of excited photons of the field in negative binomial states and photon success probability.
Counterfactual Supervision-Based Information Bottleneck for Out-of-Distribution Generalization

Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems remain. By introducing the information bottleneck (IB) principle into the learning of IRM, IB-IRM approach has demonstrated its power to solve these challenges. In this paper, we further improve IB-IRM from two aspects. First, we show that the key assumption of support overlap of invariant features used in IB-IRM is strong for the guarantee of OOD generalization and it is still possible to achieve the optimal solution without this assumption. Second, we illustrate two failure modes that IB-IRM (and IRM) could fail for learning the invariant features, and to address such failures, we propose a \textit{Counterfactual Supervision-based Information Bottleneck (CSIB)} learning algorithm that provably recovers the invariant features. By requiring counterfactual inference, CSIB works even when accessing data from a single environment. Empirical experiments on several datasets verify our theoretical results.
Kinematic Models

This chapter discusses the kinematic models of some wheeled mobile platforms and the aerial four motors helicopter-type vehicles. Such models show that commanding suitable velocities to the robots analyzed, the wheeled omnidirectional, unicycle and car-like robots and the aerial multirotor robot as well, causes velocities in the world frame, meaning that the commanded vehicles can move to any point in the navigation plane or in the navigation 3D space, in the cases of wheeled robots or aerial robots. Therefore, such models can be used to design automatic kinematic controllers, able to guide such robots in their navigation. As such controllers deal with the velocities of the robots ignoring the inertial effects, they are called kinematic controllers, and are responsible to move the vehicles in their working space. The design of such kinematic controllers, however, is not addressed here, but in Chap. 4 .