Reviewerly/exHarmony huggingface dataset:
Stable Exploration via Imitating Highly-Scored Episode-Decayed Exploration Episodes in Procedurally-Generated Environments

Exploring procedurally-generated environments is a formidable challenge for model-free deep reinforcement learning (DRL). One of the state-of-the-art exploration methods, exploration via ranking the episodes (RAPID), assigns episode-level episodic exploration scores to past episodes and makes the DRL agent imitate exploration behaviors from the highly-scored episodes. However, in complex procedurally-generated environments, such continued imitation can hinder RAPID’s performance due to the emergence of solidified episodes, i.e., episodes that remain in the highly-scored episode set due to their high scores. These solidified episodes can lead the RAPID DRL agent to overfit, hindering its exploration and performance. To address this, we design an episode-decayed exploration score, which combines the episodic exploration score and an episodic decay factor, to avoid solidifying highly-scored episodes and aid in selecting good exploration episodes. Leveraging this score, we propose exploration via imitating highly-scored episode-decayed exploration episodes (EDEE), an effective and stable exploration method for procedurally-generated environments. EDEE assigns episode-decayed exploration scores to past episodes and stores the highly-scored episodes as good exploration episodes in a small ranking buffer. The DRL agent then imitates good exploration behaviors sampled from this ranking buffer through the exploration-based sampling to reproduce these good exploration behaviors from good exploration episodes. Extensive experiments on procedurally-generated environments, specifically MiniGrid and 3D maze from MiniWorld, and sparse MuJoCo environments show that EDEE significantly outperforms RAPID in terms of final performance and sample efficiency in complex procedurally-generated environments and sparse continuous environments. Moreover, even without extrinsic rewards, EDEE maintains excellent performance in procedurally-generated environments.
Deformation estimation along a desired direction using digital speckle correlation technique

To measure deformation along a specific direction while minimizing computational steps, we propose a modified version of the digital speckle correlation technique. This technique allows for rapid estimation of deformation along the desired direction.
Learning Representations from Medical Text for Effective Diagnoses and Knowledge Discovery

Discovering knowledge and effectively predicting target events are two main goals of medical text mining. However, few models can achieve them simultaneously. In this study, we investigated the possibility of discovering knowledge and predicting diagnosis at once via raw medical text. We proposed the Enhanced Neural Topic Model (ENTM), a variant of the neural topic model, to learn interpretable representations. We introduced the auxiliary loss set to improve the effectiveness of learned representations. Then, we used learned representations to train a softmax regression model to predict target events. As each element in representations learned by the ENTM has an explicit semantic meaning, weights in softmax regression represent potential knowledge of whether an element is a significant factor in predicting diagnosis. We adopted two independent medical text datasets to evaluate our ENTM model. Results indicate that our model performed better than the latest pretrained neural language models. Meanwhile, analysis of model parameters indicates that our model has the potential discover knowledge from data.Clinical relevance— This work provides a model that can effectively predict patient diagnosis and has the potential to discover knowledge from medical text.
Real-Time Endomicroscopic Image Mosaicking with an EKF-based Sensor Fusion Approach

This study presents a real-time sensor fusion framework based on the extended Kalman filter (EKF) for accurate and robust endomicroscopic image mosaicking. The sensor fusion framework incorporates an optical tracking system that can track 6-DOF pose of the imaging probe with high accuracy in real time in conjunction with 2D local image registration from image feature matching between two consecutive frames. We evaluated the performance of the real-time image mosaicking based on the sensor fusion compared with the image or tracker only approach. As a result, it could retain the microscopic level of image detail from the image-based approach and also achieve a robust image mosaic without any drift by using the accurate optical tracking system.
PosturePose: Optimized Posture Analysis for Semi-Supervised Monocular 3D Human Pose Estimation

One motivation for studying semi-supervised techniques for human pose estimation is to compensate for the lack of variety in curated 3D human pose datasets by combining labeled 3D pose data with readily available unlabeled video data-effectively, leveraging the annotations of the former and the rich variety of the latter to train more robust pose estimators. In this paper, we propose a novel, fully differentiable posture consistency loss that is unaffected by camera orientation and improves monocular human pose estimators trained with limited labeled 3D pose data. Our semi-supervised monocular 3D pose framework combines biomechanical pose regularization with a multi-view posture (and pose) consistency objective function. We show that posture optimization was effective at decreasing pose estimation errors when applied to a 2D-3D lifting network (VPose3D) and two well-studied datasets (H36M and 3DHP). Specifically, the proposed semi-supervised framework with multi-view posture and pose loss lowered the mean per-joint position error (MPJPE) of leading semi-supervised methods by up to 15% (-7.6 mm) when camera parameters of unlabeled poses were provided. Without camera parameters, our semi-supervised framework with posture loss improved semi-supervised state-of-the-art methods by 17% (-15.6 mm decrease in MPJPE). Overall, our pose models compete favorably with other high-performing pose models trained under similar conditions with limited labeled data.
Multi-Objective Point Motion Planning for Assembly Robotic Arm Based on IPQ-RRT* Connect Algorithm

Six-axis industrial assembly robotic arms are pivotal in the manufacturing sector, playing a crucial role in the production line. The IPQ-RRT* connect motion planning algorithm for the robotic arm is proposed to improve the assembly process by reducing the time of motion planning and improving the assembly efficiency. The new IPQ-RRT* connect algorithm improves the original PQ-RRT* algorithm applied to UAVs in two dimensions by adding a node-greedy bidirectional scaling strategy. An obstacle detection range is set on the node-greedy bidirectional scaling strategy, in which the existence of obstacles is judged, and different sampling strategies are used according to the judgment results to get rid of obstacles faster, while bidirectional sampling can further improve the operation efficiency of the algorithm. In addition, effective collision detection is realized by combining the hierarchical wraparound box method. Finally, the Bezier curve is utilized to smooth the trajectory of the assembly robotic arm, which improves the trajectory quality while ensuring that the assembly robotic arm does not collide with obstacles. This paper takes the actual assembly process of an intelligent assembly platform as an example and proves the feasibility and effectiveness of the algorithm through simulation experiments and real I5 assembly robotic arm experiments.
Research on Cloud Computing Technology Graph Analysis

As a hot research field this year, cloud computing Involves various technical fields. From the perspective of the research subject, how to effectively analyze the development trend of cloud computing technology and how to formulate a cloud computing evolution route that suits Itself has become an urgent problem to be solved. Based on the classification of cloud computing products of mainstream cloud providers and consulting organizations in the Industry, this paper Introduces a method of splitting the underlying technology of cloud products, conducting product-technology two-dimensional analysis of different technologies, and providing technical research and development suggestions from the fields of cloud Infrastructure, cloud services, cloud operations and governance, and cloud security.
Shade-GAN-Based 3D Reconstruction of Missing Children Faces

Children lost from their families is a heartbreaking problem, and the process of finding a lost child is frequently challenging. The more significant issue is that families of lost children frequently have very limited image resources at their disposal. Therefore, we strive to find a way to generate more available resources with limited resources, i.e., unsupervised 3D reconstruction of human faces. In this paper, we use the Shade-GAN (Generative Adversarial Network, GAN) model for the 3D reconstruction of children's faces to achieve more realistic 3D perceptual image synthesis through the interaction of illumination and shape, simulated illumination and shadows with various illumination conditions to achieve multiple illumination constraints, and an efficient volume rendering strategy. We applied two sets of parametric models of shade-GAN on the UTK-20 dataset containing 4872 images and analyzed the degree of accuracy of the obtained results, and in the end, we concluded the applicability of shade-GAN models in the field of finding lost children and ways to improve the performance of what we obtained.
A Fair Queue Scheduling Algorithm Based on Pre-allocation of Quantum Keys

Due to the low quantum key generation rate, QKD networks face challenges with congestion forming at relay, necessitating the use of queue scheduling algorithms for effective management of relay key requests. We address the issue of fairness in queue scheduling and propose a queue scheduling algorithm, Q-GPS, which leveraging the storage capabilities of quantum keys in QKD networks. In Q-GPS, we employ a nonwork-conserving discipline to pre-allocate quantum keys for each key request packet. Service is provided only when the quantity of pre-allocated keys matches the demand of keys, which enabling the completion frequency of key request packet scheduling to approach that of corresponding packet scheduling under GPS. Q-GPS ensures a high level of fairness. The simulation results show that the Q-GPS demonstrates better Rai-Jain fairness indices compared to LD_WFQ and WF2Q under various request intensities and over different activity durations. Q-GPS enhances the fairness of the queue scheduling system throughout the entire scheduling process.
Quantum Dense Coding

Quantum dense coding is one of the most significant protocols of quantum communication. It was inspired by using quantum resources as a means to improve communications capacity and is an essential starting point for various quantum information protocols. This paper focuses on the fundamental theoretical ideas behind quantum dense coding and discusses the history of development and working protocols of it. Then, we extend two-dimensional dense coding protocol to higher dimensions and obtain the general formula of d-dimensional quantum dense coding protocol.
Fractional Fourier Transform In Time-Frequency Domain Using Quantum Memory

We present the Fractional Fourier Transform realized in quantum memory. We achieve this by applying two temporal and one spectral lens. We measure the Wigner function of a ”cat-state” and Hermite-Gaussian modes.
VisTA-LIVE: A Visualization Tool for Assessment of Laboratories in Virtual Environments

A Virtual Reality Laboratory (VR Lab) experiment refers to an experiment session that is being conducted in the virtual environment through Virtual Reality (VR) and aims to deliver procedural knowledge to students similar to that in a physical lab environment. While VR Lab is becoming more popular among education institutes as a learning tool for students, existing designs are mostly considered from a student's perspective. Instructors could only receive limited information on how the students are performing and could not provide useful feedback to aid the students' learning and evaluate their performance. This motivated us to create VisTA-LIVE: a Visualization Tool for Assessment of Laboratories In Virtual Environments. In this paper, we present in detail the design thinking approach that was applied to create VisTA-LIVE. The tool is deployed in an Extended Reality (XR) environment, and we report the evaluation results with domain experts and discuss issues related to monitoring and assessing a live VR lab session which lay potential directions for future work. We also describe how the resulting design of the tool could be used as a reference for other education developers who wish to develop similar applications.
Gaussian Mixture Conditional Tabular Generative Adversarial Network for Data Imbalance Problem

It is common for the collected data to have inconsistent numbers of some classes. The data imbalance problem causes machine learning algorithms in prediction tasks to encounter serious difficulties. To solve this issue, many effective oversampling algorithms have been proposed, but few methods pay attention to clustering analysis on data labels. In this paper, the two-stage oversampling method called Gaussian Mixture Conditional Tabular Generative Adversarial Network (GMM_CTGAN) improved based on Conditional Tabular Generative Adversarial Network (CTGAN) with the Gaussian Mixture Model (GMM) is proposed. Firstly, GMM is used as a clustering algorithm to divide the original dataset into multiple subsets. Secondly, CTGAN generates synthetic data for each class independently. Eventually, the synthetic data of all classes and original data are united to form the final training dataset. The experimental results reveal our proposed method shows more excellent performance than others and effectively solves the data imbalance problem.
Stochastic Computing-based on-chip Training Circuitry for Reservoir Computing Systems

Reservoir Computing (RC) is considered an emerging computational paradigm for the analysis of temporal data, where the training of RC systems is usually implemented through the use of a linear regression. Most RC hardware approaches perform the training stage off-chip at the server, thereby increasing the processing time, the latency and the power dissipation. This work proposes a non-iterative supervised learning method for RC systems which consists of a tropical-algebra-based regression that has been implemented in hardware using Stochastic Computing techniques. Our approach is capable of integrating a non-iterative training together with the inference in a simple and compact circuitry.
Heart Attack Prediction Using Machine Learning Classification Models

Heart disease is a chronic disease that must be taken seriously. One type of heart disease is a heart attack. Diagnosing any of these diseases requires a large number of evaluation factors and is often a tedious process for paramedics. There are many factors that must be evaluated, but it can be simplified by looking for the factors that have the most influence on this disease using the correlation matrix and selectKBest. From these two techniques, it was found that chest pain type was the variable that most related of prediction heart attack using dataset in this study. In addition, machine learning can help patients and paramedics to diagnose a heart attack by performing a classification model. This study aims to evaluate classification models for predicting heart attacks including decision trees, logistic regression, naive bayes, k-nearest neighbor, XGBoost, support vector machine (SVM), and random forest. Evaluation result of this study shows that the SVM model obtains the highest accuracy, f1-score, recall, and precision value with 85,53%, 88,17%, 95,35%, 82% respectively. Meanwhile, to see the performance of each model using the Mean Cross-Validation Score, it was found that logistic regression was the best model with a value of 0.8239.
Human robot Interaction – Object Detection and Distance Measurement Using Kinect V2

A collaborative robot is a specific type of robot that is aware of the environment, which it shares with its human operator. After the recent advancement of Industry 4.0, these robots are being rapidly adopted by the production and manufacturing industry. In comparison to a traditional industrial robot, collaborative robots offer enhanced security, process optimization, and versatility. This paper proposes a system that utilizes a time-of-flight vision camera, the Kinect V2, and an ultrasonic distance sensor to monitor the speed and separation of robots operating in shared workspaces with humans. The current issue with robots that lack additional sensors is that they stop automatically upon detecting collisions, resulting in unnecessary downtime and wear and tear on equipment. By continuously detecting the presence of human workers using the Kinect V2 and the Distance Ultrasonic sensor, this system can calculate the distances between all robot joints and humans based on joint angle information provided by robot control. Once the shortest distance, or critical distance time, is identified, the robot’s velocity and acceleration values can be adjusted to safe values. This real-time detection and distance measurement system can improve the safety of human-robot interactions and prevent collisions with people or objects. The algorithm was tested in both virtual and actual environments using VREP software and the proposed setup. By observing human skeletons and object recognition, the robot’s speed can be adjusted according to the distance at which people enter a shared workspace.
Learning-Based RF Fingerprinting for Device Identification using Amplitude-Phase Spectrograms

Radio frequency fingerprinting (RFF), a technique based on specific transmitter hardware impairments, has emerged as an effective solution for wireless device identification. In this paper, we present a flexible deep CNN-LSTM for RF feature extraction capable of handling inputs with varying lengths. We construct a channel-independent spectrogram by exploiting the amplitude and phase information of the received RF signals, ensuring the extractor’s resilience to channel variations. To evaluate the performance of the proposed approach, we utilize the open-source LoRa dataset consisting of 60 commercial off-the-shelf LoRa devices and a USRP N210 software-defined radio platform. The experimental results show that classifiers perform better when trained with RF templates generated from amplitude-phase spectrogram than amplitude-only spectrogram. This is due to the additional information present in the amplitude-phase channel-independent spectrogram.
E2E-QoE based 6G Sustainability: Challenges and Designing Aspects

As 6G mobile services will become more immersive and user customized, users will pay more attention to their subjective experience. Energy efficiency and sustainability is also one of the foremost goals in 6G system design. However, traditional network or terminal optimization based on key performance indicator or key quality indicator is difficult to accurately reflect the subjective experience of users and may be superfluous in terms of energy consumption. Then, how to reduce the energy consumption without compromising user experience to ensure sustainability is a topic worthy of studying. In this paper, we propose a novel concept of end-to-end quality of experience (E2E-QoE) based collaborative optimization, and elaborate the challenges and designing aspects of implementing such a practical framework. In order to present a potential solution to ensure the above E2E-QoE based collaborative optimization, an E2E-QoE model is developed, which can provide uniform criterion for cross-layer optimization among over-the-top services, terminals, networks, etc. Further, we apply 5G real data into several use cases to verify the effectiveness and feasibility of the proposed framework.
Augmented Reality Technology in Education: Kindergarten Education Content Trends

This article aims to investigate the trends in the development of augmented reality content used as a learning media for kindergarten or preschoolers. Based on the reviewed papers, it has been found that augmented reality applications are utilized to aid children in learning mathematics, reading, writing, recognizing letters, numbers, and prayers, as well as enhancing their cognitive abilities such as problem-solving skills, pragmatic skills, and social skills. Additionally, some augmented reality applications also help to increase the motivation and interest of children in learning. The content material displayed in augmented reality applications includes 3D images, videos, animations, animal characters, letters, numbers, and symbols. Some applications also provide interaction with devices such as accelerometer sensors or marker-based systems to improve the learning experience for children. However, further research is still required to determine the effectiveness and efficiency of augmented reality applications as a learning media for kindergarten or preschoolers.
A Simple Power Analysis of an FPGA implementation of a polynomial multiplier for the NTRU cryptosystem

As quantum computing technology advances, the security of traditional cryptographic systems is becoming increasingly vulnerable. To address this issue, Post-Quantum Cryptography (PQC) has emerged as a promising solution that can withstand the brute force of quantum computers. However, PQC is not immune to attacks that exploit weaknesses in implementation, such as Side Channel Attacks (SCAs). SCAs can extract secret keys by analyzing the physical characteristics such as power consumption of the device while performing cryptographic operation. Simple Power Analysis (SPA) is a type of SCA that uses power consumption measurements to extract sensitive information. By applying SPA to a specific hardware implementation of a PQC algorithm such as the NTRU, potential vulnerabilities can appear in the Arithmetic Unit (AU) in charge of the multiplication operation. The effectiveness of this analysis to extract sensitive information has been evaluated through extensive experiments in which different countermeasures and strategies have been proposed, as well as an accelerated algorithm has been implemented. The results demonstrate that SPA can point out security breaches in the NTRU implementation, indicating an issue that can affect the PQC in the future.
Lightweight Authentication in Edge Collaborations Utilizing Multi-dimensional Historical Information: Design and Implementation

While edge collaborations play more and more important roles in the sixth-generation (6G) network, the authentication among devices for trusted collaborations is more challenging. The existing authentication mechanisms may suffer from the long latency and high computation overhead in such application scenarios, especially when the collaboration group is large. In this paper, a lightweight group authentication scheme based on multi-dimensional historical information is proposed and implemented in a specific federated learning-based collaborative outdoor localization scenario. To further illustrate, the multi-dimensional historical information consists of the learning parameters and environment sensing data collected by lidar from the latest round of collaboration. Then, we design a key generation strategy based on the historical information and develop a group authentication protocol. In the proposed scheme, every device in the group can identify the others at once based on their broadcasting keys, which will be renewed automatically before every round of collaboration. Hence, the proposed scheme achieves lightweight group authentication and high security. Both implementation and simulation results demonstrate the validity and superior performance of the proposed scheme compared with the existing schemes.