Reviewerly/exHarmony huggingface dataset:
Research on human-vehicle gesture interaction technology based on computer visionbility

With the development of human-computer interaction technology, gesture recognition is becoming more and more important. At the same time, due to the rapid development of automotive intelligence, the introduction of human-computer interaction technology into intelligent vehicles has increasingly become an important work. Aiming at the problems of low accuracy, low recognition efficiency and weak anti-interference ability of previous gesture recognition applications in driving scenes. This paper presents an improved yolov5 algorithm. By adding the improved and optimized k-means++ clustering optimization algorithm, the problems of unstable clustering effect of K-means clustering algorithm in yolov5 model and slow convergence to large-scale data are solved. In addition, by combining the C3 module in the backbone network with the attention mechanism (CBAM), the effect of target gesture recognition under complex background is improved. Finally, the latest optimization method of loss function (EIOU) is added to the algorithm model to improve the accuracy of training convergence. The average recognition accuracy of the algorithm proposed in this paper is 4.8% higher than 88.19% of the original yolov5s algorithm when the intersection to union ratio threshold is 0.5 to 0.95. The practical availability of the improved gesture recognition algorithm is verified by the simulation scene based on ROS (robot operating system) and unity.
Improving the Quality of Control Systems According to the Criterion of Minimum Loss from the Effects of Atmospheric Pollutants

The formulation of the problem of improving software quality for decision support and management systems according to the criterion of minimum losses from harmful effects of atmospheric pollutants has been developed. An approach to compiling a system of nonlinear differential equations in the form of Cauchy, characterizing the change in the quality of software, is proposed. The results will be used to improve environmental monitoring systems for the state of the atmosphere.
Research on Trajectory Smoothness Optimization and Dynamics Simulation of Palletizing Robot

Aiming at the global optimization of the trajectory smoothness of the palletizing robot in the joint space, a trajectory planning method based on quintic polynomial interpolation including waypoints is proposed and the dynamic simulation is carried out. Taking the IRB 460 robot as the research object, the kinematics analysis is carried out, the motion trajectory is planned by using the quintic polynomial interpolation method, and the global optimization of smoothness is further completed, the MBS model of the robot is created, and the dynamics simulation is carried out to verify the effectiveness of the optimization method. The results show that the trajectory generated by this method is smoother than that of the conventional quintic polynomial interpolation method, which has certain engineering value and better scientific research significance.
Study of Improved Yolov5 Algorithms for Gesture Recognition

As a new way of human-computer interaction, gesture recognition is an important means of communication for special people. It is widely used in virtual reality system. Because yolov5 network has the advantages of high detection accuracy and high speed, it has been widely used in the field of target detection. Now it is improved based on yolov5 network, which improves the learning and presentation ability of the network by modifying the convolution layer, integrating the attention mechanism of Se channel, image enhancement, data set processing and other methods, and adds Media Pipe key point detection technology at the input to improve the generalization ability of the model. The experimental results show that compared with the improved yolov5 model, the improved yolov5 model improves the harmonic average F1 score of accuracy and recall by about 20%, recall by about 7.2%, precision by about 6.8% and average recognition accuracy MAP by about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> on the premise that the detection speed remains basically unchanged. The improved yolov5 model can better meet the requirements of real-time and accuracy in the field of gesture recognition, and can provide important theoretical basis and reference value for transplanting to portable devices and the development of human-computer interaction technology in the future.
A Particle Filtering Method for Radar Target Tracking

Observed radar measured data contains outlier and noise, thus it needs filtering. In this paper, linear and nonlinear filtering are analysed, then the state and observation equation of radar tracking are established, finally, the particle filtering process of flight tracking is derived based on the research of Bayesian estimation algorithm. The simulation effects show that the Particle Filtering algorithm converges faster and reduces the observed noise, and the tracking effect can be improved obvously.
An Anti-Multipath Radar Specific Emitter Identification Method

To address the problem that the multipath interference of intercepted radar pulse signals in practical scenarios is obvious, leading to a significant decrease in the accuracy of radar specific emitter identification. In this paper, a new radiation source fingerprint is proposed, which takes the forward quotient of the neighboring points of the pulse signal spectrum as the radiation source fingerprint, and weakens the multipath interference by improving the frequency resolution, with good anti-multipath capability. A fast fingerprint extraction method using Chirp-Z transform for local high-resolution spectrum analysis is also given. Simulation experiments show that this method performs well in overcoming multipath interference. Real data experiments classify and identify 10 radar pulse signals of the same model containing different multipath interference, and achieve more than 98% identification accuracy, which proves that the proposed method has important practical value to improve the accuracy of individual identification of radar radiation sources.
Making Hay from Wheats: A Classsourcing Method to Identify Misconceptions

Novice programmers often begin coding with a poor understanding of the task at hand and end up solving the wrong problem. A promising way to put novices on the right track is to have them write examples first, before coding, and provide them with feedback by evaluating the examples on a suite of chaff implementations that are flawed in subtle ways. This feedback, however, is only as good as the chaffs themselves. Instructors must anticipate misconceptions and avoid expert blind spots to make a useful suite of chaffs.
Refining a Risk Framework for Student Group Projects

Tertiary educators in software related topics experience issues during project-based group assessments. Our long term goal is to help educators manage the challenges that inevitably occur by providing a risk planning mechanism for students that suits the needs of the course. In this paper, we describe the first iteration of an action research study to trial a risk framework for academic group projects. Our aims were to understand how educators appropriate the framework to suit their course, gain insight into students’ understanding of risk concepts and refine the framework. A questionnaire based on the framework was distributed to students in three 12-week computer science (CS) courses. We found that most students reported few issues. Participating courses used the framework in different ways. Students lacked a holistic understanding of risk management concepts, implying the need to formally introduce these. Our study progresses the understanding of the issues experienced in academic group projects and contributes a revised framework to help manage these. In future iterations, we will investigate links between issues and project characteristics and establish a repository of mitigation strategies that is project-specific.
An Algorithm for Generating Explainable Corrections to Student Code

Students in introductory computer science courses often need individualized help when they get stuck solving programming problems. But providing such help can be time-consuming and thought-intensive, and therefore difficult to scale as Computer Science classes grow larger in size. Automatically generated fixes with explanations have the potential to integrate into a variety of mechanisms for providing help to students who are stuck on a programming problem. In this paper, we present a data-driven algorithm for generating explainable fixes to student code. We evaluate a Python implementation of the algorithm by comparing its output at different stages of the algorithm to state-of-the-art systems with similar goals. Our algorithm outperforms existing systems that can analyze and fix beginner-written Python code. Further, fixes it generates conform very well to corrections written by human experts for an existing benchmark of code correction quality.
Design of Calibration Module for a Home-based Immersive Game Using Camera Motion Capture System

Immersive exergames at home have become popular due to the advance in gaming headsets. A typical commercial gaming headset contains a head-mounted display (HMD) and a pair of hand-held controllers for interacting with virtual objects. Given this setup, an additional motion capture camera will be needed to allow full-body motion tracking. However, using the motion capture system within the immersive virtual environment (IVE) at home could be challenging since proper setup and calibration are required for a specific home environment. Hence, to simplify the camera setup and calibration issue, this work introduces the design of the calibration module for a motion capture system using the Azure Kinect camera. The calibration module allows the user to perform calibration and self-evaluate the camera setup in the immersive virtual environment. Once it has been calibrated, users can drive a full-body avatar through their body movements in the first-person perspective.
Group-based Object Alignment in Virtual Reality Environments

Group-based object alignment is an essential manipulation task, particularly for complex scenes. In conventional 2D user interfaces, such alignment tasks are generally achieved via a command/menu-based interface. Virtual reality (VR) head-mounted displays (HMDs) provide a rich immersive interaction experience, which opens more design options for group-based object alignment interaction techniques. However, object alignment techniques in immersive environments are underexplored. In this paper, we present four interaction techniques for 3 degrees-of-freedom translational alignments: AlignPanel, AlignWidget, AlignPin, and AlignGesture. We evaluated their performance, workload, and usability in a user study with 20 participants. Our results indicate different benefits and drawbacks of these techniques for group-based alignment in immersive systems. Based on the findings, we distill a set of design choices and recommendations for these techniques in various application scenarios.
Evaluation of User Interfaces for Three-Dimensional Locomotion in Virtual Reality

Locomotion is an essential factor for interaction in virtual environments. Virtual reality allows users to move freely from the constraints of gravity or the environment, which is impossible in the real world. These experiences, in which users can move at all 6 degrees of freedom, can enable new navigation paradigms that exceed 2D ground-based movement in terms of enjoyment and interactivity. However, most existing VR locomotion interfaces have limitations because they are developed for ground-based locomotion, constraining the degrees of freedom (DoF) during movement as in the real world. This exploratory study was designed to seek the features required for three-dimensional (3D) locomotion by evaluating three types of interfaces: Slider, Teleport, and Point-Tug, which were implemented based on existing ground-based locomotion techniques. We then conducted a user study with 3D navigation tasks, using both objective and subjective measures to evaluate efficiency, overall usability, motion sickness, perceived workload, and presence. The results suggest that Slider has an advantage compared to the others in terms of usability and perceived workload since it can freely and intuitively designate the direction of movement.
Studying Children’s Manipulative Gestures in Spatial Puzzle Play with VR Hand Tracking: Analysis of Goal-directed Actions

This paper presents insights about children's manipulative gestures in a spatial puzzle play (i.e. tangram) in both real and virtual environments. We present our initial work with 11 children (aged between 7 and 14) and preliminary results based on a qualitative analysis of children's goal-directed actions as one dimension of gestural input. Based on our early results, we list a set of goal-directed actions as a first stage for developing a manipulative gestural taxonomy. For a more comprehensive view, we suggest a further in-depth investigation of these actions combined with hand and finger kinematics, and outline a number of paths for future research.
Modifying Pedagogical Agent Spatial Guidance Sequences to Respond to Eye-Tracked Student Gaze in VR

In a guided virtual field trip, students often need to pay attention to the correct objects in a 3D scene. Distractions or misunderstandings of a virtual agent’s spatial guidance may cause students to miss critical information. We present a generalizable virtual reality (VR) avatar animation architecture that is responsive to a viewer’s eye gaze and we evaluate the rated effectiveness (e.g., naturalness) of enabled agent responses. Our novel annotation-driven sequencing system modifies the playing, seeking, rewinding, and pausing of teacher recordings to create appropriate teacher avatar behavior based on a viewer’s eye-tracked visual attention. Annotations are contextual metadata that modify sequencing behavior during critical time points and can be adjusted in a timeline editor. We demonstrate the success of our architecture with a study that compares 3 different teacher agent behavioral responses when pointing to and explaining objects on a virtual oil rig while an in-game mobile device provides an experiment control mechanism for 2 levels of distractions. Results suggest that users consider teacher agent behaviors with increased interactivity to be more appropriate, more natural, and less strange than default agent behaviors, implying that more elaborate agent behaviors can improve a student’s educational VR experience. Results also provide insights into how or why a minimal response (Pause) and a more dynamic response (Respond) are perceived differently.
WaveRider: Immersive Visualization of Indoor Signal Propagation

Network planners consider many factors in designing an indoor signal space to ensure a healthy network. These include material attenuation, channel overlap, peak bandwidth demand, and even humidity. Unfortunately, the current network planning software often fails to account for these factors adequately. Further, state-of-the-art network planning software either uses a few samples or limits itself to a single router. WaveRider is a mixed reality application for immersively viewing multiple routers and their signal strengths in indoor spaces to aid in network analysis. Our contribution lies in our visualization designs made to tackle domain tasks and introduce techniques, such as line integral convolution (LIC) and textons. For early direction and feedback on our visualizations, we recruited five experts in the field of signal analysis, presented WaveRider with various visual representations, and collected their feedback. Their responses show that WaveRider provides novel ways of visualizing signals, which can aid analysts in tackling real-world problems that they encounter in monitoring signal networks. Their feedback also gave us significant insights into future directions for WaveRider and other similar indoor signal space exploration systems.
Coalition Logic for Specification and Verification of Smart Contract Upgrades

It has been argued in the literature that logics for reasoning about strategic abilities, and in particular coalition logic (CL), are well-suited for verification of properties of smart contracts on a blockchain. Smart contracts, however, can be upgraded by providing a new version of a contract on a new block. In this paper, we extend one of the recent formalisms for reasoning about updating CL models with a temporal modality connecting a newer version of a model to the previous one. In such a way, we make a step towards verification of properties of smart contracts with upgrades. We also discuss some properties of the resulting logic and the complexity of its model checking problem.
Assume-Guarantee Verification of Strategic Ability

Model checking of strategic abilities is a notoriously hard problem, even more so in the realistic case of agents with imperfect information. Assume-guarantee reasoning can be of great help here, providing a way to decompose the complex problem into a small set of exponentially easier subproblems. In this paper, we propose two schemes for assume-guarantee verification of alternating-time temporal logic with imperfect information. We prove the soundness of both schemes, and discuss their completeness. We illustrate the method by examples based on known benchmarks, and show experimental results that demonstrate the practical benefits of the approach.
Explainable AI (XAI): A Survey of Current and Future Opportunities

Artificial Intelligence is the technology that is being used to develop machines that could work like humans or simply can have the intelligence relatable to that of humans. But the development of this kind of technology model that mimics humans involves a lot of complex calculations and complex algorithms that are difficult to explain and understand. For this problem, the concept of explainable artificial intelligence (XAI) is developed and introduced. It is the technology that is developed to ease the understanding process of machine learning solutions for humans. It is the concept that is being developed for making it convenient for humans to understand and interpret machine language. Black model machine learning (ML) algorithms are very hard to understand for humans who have not developed them. AI models that involve the methods like genetic algorithms or deep learning concepts are very difficult to understand. It sometimes becomes a very hard task for the domain experts too to understand the ML algorithms of the black block models, so the need for the development of this type of technology was felt. Many times, results are developed with very high accuracy are quite easy to understand for the domain experts. But Explainable artificial intelligence has a great potential to make a change in domains like finance, medicines, etc. It plays a vital role where it is important to understand the results to build trustworthy algorithms. XAI can play a great role in “third-wave AI systems” which include machines that can interact directly with the environment and that can build explanatory models that allow them to develop the characteristics of real-world phenomena. XAI has the potential to play a great role where the organizations need to build trustworthy AI models and to make them trustworthy the explainability of the AI models should be there for others as well. This technology is developed primarily to make AI understandable to those who are practitioners. This book chapter presents a wide and insightful view of XAI and its application in various fields. This chapter also includes the future scope of this technology and the need for the growth of this type of technology.
Knowledge-Based Framework for Selection of Genomic Data Compression Algorithms

The development of new sequencing technologies has led to a significant increase in biological data. The exponential increase in data has exceeded increases in computing power. The storage and analysis of the huge amount of data poses challenges for researchers. Data compression is used to reduce the size of data, which ultimately reduces the cost of data transmission over the Internet. The field comprises experts from two domains, i.e., computer scientists and biological scientists. Computer scientists develop programs to solve biological problems, whereas biologists use these programs. Computer programs need parameters that are usually provided as input by the users. Users need to know different parameters while operating these programs. Users need to configure parameters manually, which leads to being more time-consuming and increased chances of errors. The program selected by the user may not be an efficient solution according to the desired parameter. This paper focuses on automatic program selection for biological data compression. Forward chaining is employed to develop an expert system to solve this problem. The system takes different parameters related to compression programs from the user and selects compression programs according to the desired parameters. The proposed solution is evaluated by testing it with benchmark datasets using programs available in the literature.