Reviewerly/exHarmony huggingface dataset:
Augmented Reality Display of Robot with Graphs of Property Response Using Its USD Model

This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.
Reentry-phase Tracking of a Ballistic Missile in the Presence of Radar Glint Noise

A framework to overcome the effect of radar glint noise for reentry-phase tracking of a ballistic missile is presented. Glint noise is characterized by probability density functions that are heavy-tailed. For such cases, the conventional Gaussian modeling of measurement noise does not apply. A filter designed with a Gaussian noise assumption will incur a performance penalty when outliers due to glint noise are encountered. In this work, the classic Extended Kalman Filter (EKF) is modified using a robust M-estimation procedure that accounts for measurement noise distributions with heavy tails. The proposed method provides a substantial improvement (in terms of root-mean-square error) over the standard versions of EKF and UKF. All the benefits that make EKF the method of choice for state estimation (e.g. simplicity and computational efficiency) are retained in this work while robustness is added. The suggested technique is validated using simulation experiments on a three-dimensional ballistic missile reentry model.
Multimodal Fusion and Data Augmentation for 3D Semantic Segmentation

Since modern autonomous driving (AD) platforms offer a variety of sensors, it is intuitive to leverage complementary data from multimodal sensors to produce reliable 3D semantic segmentation. However, due to the information loss and the sub-optimized fusion in multimodal fusion methods, LiDAR-only methods currently occupy the top positions in the leaderboard of datasets. In this paper, we focus on two aspects to improve the LiDAR-camera fusion semantic segmentation performance, namely data augmentation and fusion strategy. First, we propose an novel data augmentation by refining point-image patches. Second, we design an attention fusion block for the dual-branch segmentation network by considering the modality gap between LiDAR and RGB camera. Experiments on nuScences indicate that our proposed method outperforms the baseline methods on key classes.
Target Speaker Recognition in The Cocktail Party

The arise of the deep learning techniques has accelerated the advance of the speaker recognition and the increase in personalized devices weighted the importance of the target speaker recognition (TSR). More precisely, it is important to recognize the target speaker correctly even when a variety of speakers utter at the same time. In this paper the TSR methods are proposed and evaluated in the multi-speaker environments: (1) TSE is performed before the speaker recognition on the input voice; (2) results from (1) and the speaker recognition are fused. Among the proposed methods, the latter method showed the better results; more precisely, the fusion based method showed the relative performance improvements of at least 11% from the ordinary speaker recognition system.
A Survey on Autonomic Multi-cloud Computing

Multi-cloud computing has emerged in the last years to address problems like avoiding vendor lock-in and improving resilience in cloud computing. Recent reports have shown a growing interest from companies in this type of technology. On the other hand, architecting and managing a multi-cloud environment is more complex and brings challenges to be tackled. This work aims to understand how autonomic computing (AC) can help to solve multi-cloud problems. We surveyed six research databases and applied a methodology using objective criteria to select studies and analyze them. The results showed that complexity is the main reason for using AC. In addition, we found that most solutions are reactive and use static thresholds. Among the challenges, we noticed that a factor is the lack of standardization in both areas.
Trend Analysis of Registration Technology Between Infrared Images

Image Registration, a process of aligning the same area by grasping the relationship between the input image and the reference image, has recently been researched in various fields. In this paper, we investigated the types of Image Registration and each method. In addition, while investigating recent trends in Image Registration, we felt a lack of R&D for IR-IR Image Registration and analyzed its cause.
Managing Digital Objects with Decentralised Identifiers based on NFT-like schema

The diversity (text, images, algorithms, etc.) and the ambiguity of data sovereignty and privacy make the management of digital objects very challenging. Users need a unified and convenient way to manage their digital objects. This places a high demand on the findability and interoperability of the management model. In recent years blockchain has provided a new route to an open and secure platform due to its attributes such as distributed, traceable, and tamper-evident. In this paper, a new NFT-like scheme is proposed, which uses metadata converts digital assets into digital object identifiers, and transforms digital objects that require clear sovereignty into NFTs to ensure the authenticity and uniqueness of ownership. Our scheme can facilitate the dynamic management of digital objects using smart contracts.
Blockchain-based Cross-organizational Workflow Platform

Data-centric workflows across organizations are gaining more and more popularity. To automate this process, the traditional approaches centralise related data from different organizations to the cloud, and then use a workflow engine to complete cross-organizational collaboration. There are limitations of those approaches, such as the requirement of data centralisation which could lead to the leakage of critical data. In this work, we present a workflow platform for consuming distributed data based on Kubernetes and JupyterFlow, and use blockchain technology to guarantee security and privacy through empowering the data owner with control of their own data. To reduce replication and network throughput, the blockchain only contains the meta data referring to the data in off-chain storage. We develope a JupyterHub extension to support data registration and query, and used the RESTFul API to connect the web application with the blockchain network. Finally, we demonstrate a simple data processing use case as proof-of-concept to validate our proposed platform.
Analyzing Transformers via Value Matrices

We propose a new method to analyze Transformer language models. In Transformer self-attention modules, attention weights are calculated from the query vectors and key vectors. Then, output vectors are obtained by taking the weighted sum of value vectors. While existing works on analysis of Transformer have focused on attention weights, this work focused on value and output matrices. We obtain joint matrices by multiplying both matrices, and show that the trace of the joint matrices are correlated with word co-occurences.
NFT as a proof of Digital Ownership-reward system integrated to a Secure Distributed Computing Blockchain Framework

Today, the global economy is dependent on the Internet and computational resources. Although they are tightly interconnected, it is difficult to evaluate their degree of interdependence. Keeping up with the pace of technology can be a challenging task, mainly when updating the hardware and software infrastructure. Every day, corporations and governments are faced with this issue; most have been victims of cyber attacks, security breaches, and data leaks. The consequences are significant in monetary losses; damage remediation is unattainable, even impossible, in certain circumstances. The repercussions might include reputational damage, legal responsibility, and threats to national security (when attacks are carried out against critical infrastructures to control the resources of a country), to name a few. Similarly, data has become such an integral part of many industries that it is one of the most critical targets for attackers that often is encrypted by ransomware, stolen, or corrupted. Without data, many companies are not able to continue operating as they do. The combination of all these factors complicates the ability of organizations to cooperate, trust, and share information in efforts to research and develop solutions for industry and government.This work proposes a Blockchain-based infrastructure solution provided by “Hyperledger Fabric” technology for companies to securely transmit and share information using the latest encryption and data storage technologies operating on the model of distributed systems and smart contracts. By presenting unique digital assets as Non-Fungible Tokens (NFT), the infrastructure is able to trust the integrity of the data, while protecting it from counterfeiting. Through the use of a Blockchain-based file storage system known as IPFS, and by connecting all the relevant elements together through a web-based application, it is possible to demonstrate that the implementation of such systems is feasible, highly scalable and a useful tool that many organizations can utilize to create new work systems and worktflows for digital asset management.
A Hybrid Decision-making Approach to Security Metrics Aggregation in Cloud Environments

In cybersecurity, being able to quantity the level of security has been a long quest so that decisions can be made toward improving security. Various metrics have been proposed and applied, which can usually be computed from collected measurements. However, only certain aspects of the target system are measured corresponding to the purpose the metrics were designed for, be it software vulnerabilities or configuration errors, thus lacking a concise and clear image of the overall security of a system for the practitioners to act on, especially when it comes to large-scale or complex systems.We argue that overall security metrics are defined by humans based on specific security goals before they can be computed. Therefore, we propose a hybrid approach to the aggregation of well-established individual security metrics by combining machine computation with human decision making. In particular, we modify the Analytic Hierarchy Process (AHP) to reach a group decision of selected “experts”, which can derive the weights of individual metrics for their aggregation. We showcase its feasibility by selecting several common metrics to measure the target systems in our testbed, and conducting an AHP survey with seventeen experts. The resulted overall security score for the target systems shows how our approach enables comparison of the overall security between those systems. By considering cloud-oriented settings, we also showcase how this approach can be applicable to today’s virtualized environments.
Absolute wavefront measurement of lithography projection lens using random averaging method

With the development of science and technology, the industry has an increasing demand for high-precision optical detection, such as wave aberration detection of the lithography objective lens, the core component of the lithography machine . System errors inevitably exist in the process of optical detection. Because the random averaging method has the characteristics of simple operation, this paper studies the system error calibration of the measuring device based on the random sphere method. Rotate the direction of the ball lens with a certain accuracy at random in the optical path, eliminate the random error in the measurement system and the uneven error of the spherical lens surface on average, and then subtract the inherent spherical aberration of the ball lens from the average measurement data to realize the measurement. Self-calibration of device systematic errors. The experimental platform for the verification of this method includes: a 632.8 nm light source, a 10 μm pinhole, a common lens, a spherical lens with a refractive index of 1.52 and a diameter of 40 mm, and a SID4 camera. After experimental verification, the method can realize the purpose of calibrating the system error of the measuring device with high precision and low cost by using a low-precision ball lens.
Optimization algorithm for anti-collision planning of fiber elements based on divide-and-conquer weighting

Lamost can precisely locate 4,000 optical fibers according to the star catalog position within a few minutes, which greatly improves the observation efficiency. There is no blind area, and the observation area of the fiber optic unit must be overlapped, but this will cause the adjacent units to collide during the movement process. Once the collision occurs, the observation accuracy and service life of the unit will be reduced. In order to solve the collision problem between optical fiber units, this paper proposes an anti-collision planning scheme for optical fiber units based on the idea of divide and conquer weighting. This scheme simplifies the overall collision problem into a small set of collision problems. The unit sequential is adjusted, and simulation experiments show that, compared with the commonly used path planning scheme, this method can better improve the observation efficiency of astrological phenomena and reduce the processing times of the unit.
Saving Bits Using Multi-Sensor Collaboration

In this paper, we propose a new video coding method that saves bits using multi-sensor collaboration. Traditional video coding methods have saved bits by removing redundancy in videos. Recently, multiple types of sensors are being deployed to many solutions and multi-sensor data have significant advantages over single sensor data. The proposed method suggests a new way of video compression that saves bits using multi-sensor collaboration. We apply multi-sensor collaboration to the 3D video coding based on color and depth sensors. Based on the correlation between color and depth images, we design two networks CNN-US and CNN-QE in the proposed video coding method to achieve up-sampling and quality enhancement, respectively. The proposed method combines CNN-US and CNN-QE with 3D-HEVC to save bits using multi-sensor collaboration. Compared with 3D-HEVC anchor, the proposed method achieves average 5.9%, 66.8%, and 71.0% BD-rate reductions for sampling factors 1, 2, and 4 on the depth videos of 3D-HEVC test dataset, respectively.
Evaluation of sensory discomfort caused by stimulation with virtual reality in volunteers with and without kinetosis

ABSCTRACT Purpose to compare the symptoms of motion sickness caused by virtual reality stimulation in volunteers with and without history of the disease. Methods qualitative and analytical, observational, cross-sectional, and prospective study, approved by Research Ethics Committee, 3.443.429/19, with volunteers with and without history of motion sickness who were subjected to immersion in VR with the use of virtual reality glasses. Before and after sensory stimulation, the participant had respiratory rate (RF), heart rate (HR) and systolic blood pressure (PAs) and diastolic blood pressure (PAd) measured. On the first day, the volunteer was exposed to a video that simulated a person in a car, with a predominance of lateral visual flow. A week later, an animation of a roller coaster, with a predominance of frontal visual flow. During the 10-minute experiment, a score from 0 to 10 was given every 30 seconds for the intensity of the discomfort felt by the volunteer. A post-questionnaire was conducted to assess motion sickness symptoms. Results individuals with motion sickness history had a higher intensity of symptoms in the car (p = 0.026) and roller coaster experiment (p = 0.035). There was no correlation between motion sickness and the variables HR,FR,PA. Patients with motion sickness gave higher scores of discomfort throughout the experiments, mainly in the roller coaster experience. Conclusion individuals with motion sickness present more intense symptoms when subjected to stimuli by VR compared to controls without disease.
On the predictability in reversible steganography

Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most advanced reversible steganographic algorithm for digital images. The pivot of this algorithm is predictive analytics in which pixel intensities are predicted given some pixel-wise contextual information. This task can be perceived as a low-level vision problem and hence neural networks for addressing a similar class of problems can be deployed. On top of the prior art, this paper investigates predictability of pixel intensities based on supervised and unsupervised learning frameworks. Predictability analysis enables adaptive data embedding, which in turn leads to a better trade-off between capacity and imperceptibility. While conventional methods estimate predictability by the statistics of local image patterns, learning-based frameworks consider further the degree to which correct predictions can be made by a designated predictor. Not only should the image patterns be taken into account but also the predictor in use. Experimental results show that steganographic performance can be significantly improved by incorporating the learning-based predictability analysers into a reversible steganographic system.
Improving automated thyroid cancer classification of frozen sections by the aid of virtual image translation and stain normalization

Frozen sections are rapidly generated during surgical interventions. This allows surgeons to wait for histological findings during the interventions in order to base intra-surgical decisions on the outcome of the histology. However, compared to paraffin sections the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep learning-based image translation technology facilitates a virtual conversion between different native imaging technologies with the potential of translating a frozen section into a virtual paraffin section. Stain normalization can be applied to adjust further unequal image characteristics. We investigated the effect of deep learning-based image translation, conventional image normalization and a combination of these techniques on computer aided decision support systems for thyroid cancer diagnostics. For classification, a bag-of-words approach, based on convolutional neural network features, k-means clustering and a support vector machine were employed. While stain normalization led to a decreased overall classification accuracy (0.703 vs 0.727), image translation led to an increased mean score (0.770). A combination of both, image translation and normalization increased the accuracy even further (0.844) and clearly reduced the gap to the post-operative paraffin sections (0.902). Deep learning-based image translation proved to be a powerful tool to enhance accuracy of computer aided diagnosis which clearly outperformed conventional stain translation. This work provides a strong motivation for performing a study with expert pathologists performing the categorization of frozen sections and the corresponding improved sections, to investigate whether a similar effect is achieved in a clinical setting.
Graph neural network modeling for ethane recovery evaluation in RSV process

In the oil and gas industry, the Recycle Split Vapor (RSV) process is a widely-used technology for ethane recovery. It is of particular importance for industrial production to continually track ethane recovery affected by the running status of RSV. The commonly adopted method for this purpose is to aperiodically sample the output gas for laboratory test, which is time consuming and not in time. To address this problem, in this work we develop a graph neural network model for ethane recovery evaluation, which aims at diagnosing ethane recovery quality automatically and timely according to the temperature variations of the sensor array on the demethanizer. Specifically, we use a graph attentional neural network to establish a map between ethane recovery quality and the overall temperature dynamics of the sensors distributed on the demethanizer, where the sensors are abstracted by graph nodes and the spatial connections between sensors are denoted by the edges in the graph so that the temporal and spatial correlations between various temperature sensors on the demethanizer can be captured and the features of temperature vibration can be learned. By leveraging the learned features, the classifier makes “qualified/unqualified” prediction for ethane recovery quality. Moreover, the abnormal sensors are located based on the learned attention scores on the edges, which may guide the operators to coordinate the corresponding valves. The results on extensive experiments show that the method can effectively evaluate the quality of ethane recovery and infer the sensor points that may cause the disqualification.
Inner crossover fusion network with pixel-wise sampling for dynamic scene deblurring

Despite dynamic scene deblurring methods based on multi-scale or scale-recursive or hierarchical multi-patch strategy have made great progress, they still have some shortcomings. In the multi-scale or scale-recursive networks, bicubic down-sampling operation may cause some important high frequency information loss, e.g., strong edges. In addition, these networks are limited to using the same receptive field information while neglecting the cross-scale and cross-receptive field relationships inside the subnetwork, which may lead to information drop-out. To alleviate the above problems, we propose an inner crossover fusion network with pixel-wise sampling, called ICFNet, for dynamic scene deblurring. More specifically, we first design a pixel-wise sampling operation to replace bicubic down-sampling in the multi-scale or scale-recursive networks, so as to avoid the loss of the strong edge information. In addition, different from multi-scale and multi-patch architecture, we design a novel inner crossover fusion architecture to make full use of blur information with cross-scale and cross-receptive field in the network. To maximize the advantages of the ICFNet architecture, we design three region-enhanced residual blocks with different receptive fields for learning multiple receptive field features. Finally, extensive experimental results on different datasets have demonstrated that the proposed network can obtain better deblurring effect.