Reviewerly/exHarmony huggingface dataset:
Learning Cooperative Behaviours in Adversarial Multi-agent Systems

This work extends an existing virtual multi-agent platform called RoboSumo to create TripleSumo -- a platform for investigating multi-agent cooperative behaviors in continuous action spaces, with physical contact in an adversarial environment. In this paper we investigate a scenario in which two agents, namely `Bug' and `Ant', must team up and push another agent `Spider' out of the arena. To tackle this goal, the newly added agent `Bug' is trained during an ongoing match between `Ant' and `Spider'. `Bug' must develop awareness of the other agents' actions, infer the strategy of both sides, and eventually learn an action policy to cooperate. The reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) is implemented with a hybrid reward structure combining dense and sparse rewards. The cooperative behavior is quantitatively evaluated by the mean probability of winning the match and mean number of steps needed to win.
Multiobjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis

Disease prediction is very much important as well as crucial for the patients and also for health informatics. If the disease is detected early, then it is easy for the medical practitioners to treat early and assists them in taking more functional and preventive steps. Feature selection (FS) is being taken as a preprocessing step used to eradicate the features that are influencing negatively the efficacy of the machine learning (ML) techniques. The efficacy may be negative because of the irrelevant or may be redundant features in the dataset. Intelligent models including classification, clustering, regression, and boosting are helpful in extracting useful knowledge. Nowadays, there is an explosion in medical data, and there is also an explosion in computational technologies. Most of the medical datasets are high dimensional in nature, and so there is a need for optimal FS which is a hard problem. Hence, this chapter uses an MOEA built on decomposition (MOEA/D), which takes care of the FS in classifying medical diagnosis. This chapter presents the mathematical model of MOEA/D and its use for the FS process in medical diagnosis. The behavior of (MOEA/D) method is examined with various well-known state-of-art multiobjective optimization methods and on most of the datasets.
Privacy‐enhancing machine learning framework with private aggregation of teacher ensembles

Private aggregation of teacher ensembles (PATE), a general machine learning framework based on knowledge distillation, can provide a privacy guarantee for training data sets. However, this framework poses a number of security risks. First, PATE mainly focuses on the privacy of teachers' training data and fails to protect the privacy of their students' data. Second, PATE relies heavily on a trusted aggregator to count teachers' votes, which is not convincing enough to assume a third party would never leak teachers' votes during the knowledge transfer process. To address the abovementioned issues, we improve the original PATE framework and present a new one that combines secret sharing with Intel Software Guard Extensions in a novel way. In the proposed framework, teachers are trained locally, then uploaded and stored in two computing servers in the form of secret shares. In the knowledge transfer phase, the two computing servers receive shares of private inputs from students before collaboratively performing secure predictions. Thus neither teachers nor students expose sensitive information. During the aggregation process, we propose an effective masking technique suitable for the setting to keep the prediction results private and prevent the votes from being leaked to the aggregation server. Besides, we optimize the aggregation mechanism and add noise perturbations adaptively based on the posterior entropy of the prediction results. Finally, we evaluate the performance of the new framework on multiple data sets and experimentally demonstrate that the new framework allows highly efficient, accurate, and secure predictions.
Server‐aided multiparty private set intersection protocols for lightweight clients and the application in intelligent logistics

In numerous data application scenarios, various data can be represented in the form of data sets, and the intersection is often the common concern of multiple users. Using private set intersection (PSI) protocol, users can securely compute the intersection of their sets without disclosing their private input and other additional information. At the same time, there is also a strong practical demand for statistical analysis of intersection data. However, when multiple parties are involved, the efficiency of the multiparty PSI protocol decreases dramatically as the number of users increases. In this paper, we construct a novel server-aided multiparty PSI protocol, which can transform the complex multiparty computation problem into an efficient two-party computation problem. In addition, we design a series of server-aided party PSI statistical protocols to compute the statistics of the intersection elements, such as the sum, average, variance, range (maximum, minimum), and the cardinality of intersection (the size of intersection). In our protocol, the clients only need to upload their private data to the servers in blinded form and do not need to keep online during server computing. Experiments show that our protocol has high computation and communication efficiency and is suitable for lightweight clients. In addition, we also introduce an application of our protocols in the field of intelligent logistics.
Team Tidyboy at the WRS 2020: a modular software framework for home service robots

This paper describes Team Tidyboy's approach to build a functional, robust, yet highly modular software framework for home service robots. Home service tasks require a number of software modules to work together, which includes verbal and nonverbal human robot interactions, 3D perception of known and unknown objects and manipulation of various objects using movement of both the manipulator and the base of the robot. World Robot Summit (WRS) 2020 Partner Robot Challenge (PRC) rule has raised the bar higher by asking for a reliable, long-term operation of such a system without human intervention. We present how we have overcome such challenges by robust software modules, repeated experiments in randomly initialized simulated environments, and finally introduction of various failure detection and recovery algorithms. Our software is successfully validated in RoboCup@Home 2021 and WRS 2020 PRC remote participation league, which led us to obtaining the first places in both competitions.
Evaluation of Point Cloud Streaming and Rendering for VR-Based Telepresence in the OR

Immersive and high-quality VR-based telepresence systems could be of great benefit in the medical field and the operating room (OR) specifically, as they allow distant experts to interact with each other and to assist local doctors as if they were physically present. Despite recent advances in VR technology, and more telepresence systems making use of it, most of the current solutions in use in health care (if any), are just video-based and don’t provide the feeling of presence or spatial awareness, which are highly important for tasks such as remote consultation, -supervision, and -teaching. Reasons still holding back VR telepresence systems are high demands regarding bandwidth and computational power, subpar visualization quality, and complicated setups. We propose an easy-to-set-up telepresence system that enables remote experts to meet in a multi-user virtual operating room, view live-streamed and 3D-visualized operations, interact with each other, and collaboratively explore medical data. Our system is based on Azure Kinect RGB-D cameras, a point cloud streaming pipeline, and fast point cloud rendering methods integrated into a state-of-the-art 3D game engine. Remote experts are visualized via personalized real-time 3D point cloud avatars. For this, we have developed a high-speed/low-latency multi-camera point cloud streaming pipeline including efficient filtering and compression. Furthermore, we have developed splatting-based and mesh-based point cloud rendering solutions and integrated them into the Unreal Engine 4. We conducted two user studies with doctors and medical students to evaluate our proposed system, compare the rendering solutions, and highlight our system’s capabilities.
Facial Emotion Recognition of Virtual Humans with Different Genders, Races, and Ages

Research studies suggest that racial and gender stereotypes can influence emotion recognition accuracy both for adults and children. Stereotypical biases have severe consequences in social life but are especially critical in domains such as education and healthcare, where virtual humans have been extending their applications. In this work, we explore potential perceptual differences in the facial emotion recognition accuracy of virtual humans of different genders, races, and ages. We use realistic 3D models of male/female, Black/White, and child/adult characters. Using blendshapes and the Facial Action Coding System, we created videos of the models displaying facial expressions of six universal emotions with varying intensities. We ran an Amazon Mechanical Turk study to collect perceptual data. The results indicate statistically significant main effects of emotion type and intensity on emotion recognition accuracy. Although overall emotion recognition accuracy was similar across model race, gender, and age groups, there were some statistically significant effects across different groups for individual emotion types.
Controlling Continuous Locomotion in Virtual Reality with Bare Hands Using Hand Gestures

Abstract Moving around in a virtual world is one of the essential interactions for Virtual Reality (VR) applications. The current standard for moving in VR is using a controller. Recently, VR Head Mounted Displays integrate new input modalities such as hand tracking which allows the investigation of different techniques to move in VR. This work explores different techniques for bare-handed locomotion since it could offer a promising alternative to existing freehand techniques. The presented techniques enable continuous movement through an immersive virtual environment. The proposed techniques are compared to each other in terms of efficiency, usability, perceived workload, and user preference.
SightX: A 3D Selection Technique for XR

Many 3D Interaction techniques use virtual ray-casting for general selection and manipulation tasks. Some virtual ray-casting techniques are equipped with gadgets to fit specific interaction scenarios, such as selecting an object with special shapes, or a part of an object. These techniques are intuitive and largely successful. However, there are still some common situations under which the capabilities of virtual ray-casting are limited. When a user needs to select an object that is far away and small, the movement of the ray on the far end greatly amplifies hand movement of the user, which in turn results in inefficient and inaccurate selection operations. Moreover, in an Extended Reality (XR) space, especially in an Augmented Reality (AR) space, where a user’s ability of performing teleportation or shifting to another view is limited, selecting an object that is occluded or from a cluster of objects with high density would be less flexible. We developed and evaluated SightX, a virtual ray-casting mechanism augmented with a remote anchor and a sight view with see-through capability for XR interactions. Our user studies suggested this design can significantly improve the performances and user experiences over the standard virtual ray-casting for 3D objects selection tasks.
Analysis of COVID-19 Epidemic Disease Dynamics Using Deep Learning

COVID-19 has induced anxiety, depression, and fear among people around the world with its cases. During this period, people undergo mixed emotion. Social media is a tool that affected human life during this time in a dominant manner. Twitter is a trending social media platform. Analyzing sentiment of tweets related to COVID-19 can help to analyze the sentiments around the world. In this system, we have taken the dataset which contains tweets related to COVID-19 from IEEE dataport. SVM and LSTM models are built which classifies the tweets as positive, negative, and neutral accordingly. The performance of LSTM model is further analyzed by using hyperparameter tuning method. LSTM gave better results than SVM. It gave an accuracy of 94.58%.
A Modified Whale Optimisation Algorithm to Solve Global Optimisation Problems

Whale optimization algorithm (WOA) is a novel and competitive swarm-based optimisation method that exceeds several previous metaheuristic algorithms in terms of simplicity and efficiency. Whale optimisation algorithm, a revolutionary nature-inspired algorithm, which mimics the behaviour patterns of humpback whales. WOA will interference with local optimization and greatly reduce accuracy for global optimization issue. To solve this type of problem, in this work, a new update equation has been developed named as modified whale optimisation algorithm (MWOA). Also, MWOA has been tested some CEC 2005 benchmark functions with dimension ranging from 2 to 30. The experimental outcomes show that the MWOA produce improved outcomes in terms of optimum value, convergence speed, and stability.
Unbiased Conversion of 3-D Bistatic Radar Measurements to Cartesian Position

Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for three-dimensional (3-D) bistatic radar, to a nonellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact lens, that causes consistency problems for a tracking filter. A solution is proposed by developing an unbiased and statistically consistent conversion of the bistatic 3-D sine-space position measurements to Cartesian coordinates, based on second-order Taylor expansion. Such an approach was successfully used for monostatic radars but considered impractical for the bistatic case due to the difficulty to derive explicit conversion expressions, as done in this article. The recently developed 2-D approach for the bistatic case could not be successfully extended to 3-D and a different conversion was needed. It is shown that in contrast to the first-order conversion and the best nonlinear conversions (based on the cubature method), only the proposed conversion can cover the true uncertainty.
Smart Contracts Security Threats and Solutions

Blockchain-enabled smart contracts are subjected to several issues leading to vigorous attacks such as the decentralized autonomous organization (DAO) and the ParitySig bug on the Ethereum platform with disastrous consequences. Several solutions have been proposed. However, new threats are identified as technology evolves and new solutions are produced, while some older threats remain unsolved. Thus, the need to fill the gap with a more comprehensive survey on existing issues and solutions for researchers and practitioners arises. The resulting updated database will become an essential means for choosing a particular solution for a specific subject. In this review, the authors embrace mainly codifying security privacy and performance issues and their respective solutions. Each problem is attached to its corresponding solutions when they exist. A summary of the threats and solutions is provided as well as the relationship between threat importance and the given answers. They finally enumerate some directives for future works.
Cultivation Design of Applied Undergraduates’ Engineering Innovation Ability Based on Virtualization Technology

The development of virtualization technology has promoted the development of computers in education, and the reform of basic education courses has promoted profound reforms in science education. The integration of virtualization technology and science teaching is an inevitable trend in the development of information technology and science education, and it is also training the inevitable requirement of talents with scientific culture and innovation ability. This article takes undergraduates as the research object and aims at the application characteristics of local higher education institutions, combined with the purpose of higher technical personnel training, application practical ability, and innovation ability, to establish a training system for college students with innovation ability suitable for engineering application undergraduate talent training mode, and improve students’ research and innovation ability and engineering practice ability. This article proposes to organically integrate virtualization technology with undergraduate students’ engineering innovation ability training courses, explore virtual technology experimental models, cognitive models, promote student self-study and student mutual learning, where students actively participate, and strengthen student innovation and creativity training. Improve the ability of design and transform teacher education into student-centered. The results of the study show that students’ innovative ability generally accounts for 56%, and the effects of innovative activities organized by schools generally account for as high as 70%. Students are generally not very enthusiastic about innovation.
Region-aware neural graph collaborative filtering for personalized recommendation

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A 3D reconstruction based on an unsupervised domain adaptive for binocular endoscopy

In minimally invasive surgery, endoscopic image quality plays a crucial role in surgery. Aiming at the lack of a real parallax in binocular endoscopic images, this article proposes an unsupervised adaptive neural network. The network combines adaptive smoke removal, depth estimation of binocular endoscopic images, and the 3D display of high-quality endoscopic images. We simulated the smoke generated during surgery by artificially adding fog. The training images of U-Net fused by Laplacian pyramid are introduced to improve the network's ability to extract intermediate features. We introduce Convolutional Block Attention Module to obtain the optimal parameters of each layer of the network. We utilized the disparity transformation relationship between left- and right-eye images to combine the left-eye images with disparity in HS-Resnet to obtain virtual right-eye images as labels for self-supervised training. This method extracts and fuses the parallax images at different scale levels of the decoder, making the generated parallax images more complete and smoother. A large number of experimental research results show that the scheme can remove the smoke generated during the operation, effectively reconstruct the 3D image of the tissue structure of the binocular endoscope, and at the same time, preserve the contour, edge, detail, and texture of the blood vessels in the medical image. Compared with the existing similar schemes, various indicators have been greatly improved. It has good clinical application prospects.
Application of Visual Servo in Tracking and Grasping Moving Target

This paper presents a visual servoing system for the task of tracking and grasping a moving target in 3D space. A multiple-cue feature visual tracking module combining known 3D CAD models of the object is used to extract the target pose in the RGB-D images. An Extended Kalman Filter (EKF) is developed to further compensate for the delay of the system and estimate the object motion. A two-stage controller for the visual servo is proposed with the first part being able to control the gripper to track the target, while the second part is a pose based visual servo (PBVS) scheme for the grasping motion. Experiment results show that the presented system is able to perform real time tracking- grasping tasks to a moving target.
Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Image stitching in 3D film production synchronizes multiple independent frame images as a single high-resolution output. The stitching process is common for displaying a large coverage or shot in a single frame containing distinct images. For improving the realistic accuracy of 3D film images, this manuscript introduces an Itinerant Pixel-Matching-based Stitching Process (IPMSP). The proposed stitching process relies on cross-sectional pixels identified in merging two or more images. Based on the linear cumulative distribution in augmenting images, the homogeneity feature is identified. If the homogeneity is high then the stitching for linear pixels occurs increasing the frame resolution and size. The cumulative distribution is determined using the recurrent neural network in verifying homogeneity and contrast. For the contrast pixels identified, the cross-sectional matching is performed for substituting similar pixels in the missing sections. The process is repeated until the stitching region is the same based on the homogeneity feature and increased dimensions. Therefore, this process is capable of improving accuracy, precision, substitution, and reducing errors and complexity.
Key for a Hidden Quantum State

Quantum trajectories are crucial to understanding the evolution of open systems. We consider an open cavity mode undergoing up and down multistate quantum jumps due to the emission and absorption of photons. We prove that among all subtrajectories, starting simultaneously from different photon number states, only one survives a long single-run evolution. A random Fock state terminating the subtrajectory becomes known for the ergodic case via the key-the processed record of the input and output photocounts, and the trajectory duration. Based on this result, we propose a robust protocol to infer the Fock state, a valuable resource for quantum applications.
A hybrid approach for enhancement of abdominal CT images

In this technological era, digital imaging is integrated into every aspect of our life, and it has a predominant and essential role in medical investigations. Different types of diseases are being diagnosed using Computed Tomography (CT) images, acquired with varying bit depths, and kept in conventional Digital Imaging and Communications in Medicine (DICOM) format. However, one of the key challenges faced by the practitioners experimenting in the field of biomedical image analysis is that the images acquired are of low contrast. This research work aims to propose a method centered on the windowing technique and anisotropic diffusion filter to enhance the contrast of medical images for further analysis. The suggested approach does adjustments of contrast and brightness using the information extracted from the DICOM header of considered abdominal CT images to efficiently visualize the details for further processing. Furthermore, we have analyzed and compared its performance with distinct notable techniques which have been typically used for the enhancement of the medical image. The recorded outcomes revealed that the suggested algorithm gives better results with average values of Peak Signal to Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE), and Feature Similarity (FSIM) index equivalent to 27.71 dB, 8.38, and 0.96, respectively. Further, the average value of the Universal Image Quality (UIQ) index and Edge Content (EC) is equivalent to 0.83 and 9.37, respectively. Based on the experimental results performed on 15,000 images of a real human CT dataset, we have observed that the suggested algorithm gives much better results from subjective and objective perspectives.