Reviewerly/exHarmony huggingface dataset:
Effects of Augmenting Real-Time Biofeedback in An Immersive VR Performance

Currently, the most common form of Virtual Reality (VR) performance is that the performer wearing motion capture devices while the audience are immersed in a virtual environment remotely. Augmenting real-time biosignals provide the possibility of enhancing audience's perceptual experience in a collaborative virtual environment. In this research, we focus on exploring two different augmentation methods based on real-time biofeedback from the performer and the audience during a live VR performance. We propose a support system to investigate how performer's heart rate (HR) and audience's electroencephalogram (EEG) visualizations, and the corresponding auditory feedback, influence audience's sense of perception, immersion, and the narrative understanding. The results show that the performer's heart rate visual biofeedback significantly produced more concentration of the participants and the audience's EEG visual biofeedback significantly increased the participants' ability of narrative understanding.
A Survey of Image Caption Tasks

Image caption technology is a new task in the field of computer vision after relay image recognition, image segmentation and object detection. This technology can input the image of the computer, through the mathematical model and the powerful calculation of the computer, and finally output the descriptive text that is highly related to the content of the image. In order to have a clear understanding of the current research status of image caption tasks and look forward to the future, this paper reviews the research progress of image caption tasks. In order to have a clear understanding of the current research status of image caption tasks and look forward to the future, this paper reviews the research progress of image caption tasks. This paper summarizes related methods, applicable datasets, and evaluation metrics for image caption tasks.
Benchmarking and Analysis of Noisy Intermediate-Scale Trapped Ion Quantum Computing Architectures

Quantum computing is at the cusp of showing relevance for real-world problems. However, currently available devices are small in scale, and hardware demonstrations have shown limited applicability to scientific and commercial problems of interest. In this work, we investigate a set of application-specific noisy intermediate-scale quantum (NISQ) algorithms varying in size from 4-80 qubits, and use these benchmarks to evaluate trade-offs in the design of trapped ion quantum computing architectures. We have developed a tool-chain consisting of architecture specific compilation and simulation tools and used these tools to target five different candidate trapped ion quantum computing architectures. Using our tool-chain, we can estimate metrics such as application run-times and overhead. Additionally, we use our tools to determine the critical operations of an architecture and to study the sensitivity to architectural constraints in particular implementations. Our tools are designed to be flexible, allowing us to study a broad range of benchmarks, hardware architectures, physical constraints, and operation timing.
Metaverse and Internet Youth in China

The rapid development of the Metaverse will certainly affect the work, study and life of internet youth in China. The cause and purpose of this paper is make the Metaverse better serve the growth of internet youth in China, so that the Metaverse can not only meet their personalized needs, but also can guide them to make a contribution to the modern powerful country. In order to make internet youth in China make more outstanding contributions to the country’s prosperity and prosperity on the new journey of building a modern socialist power in the future, China’s educational circle needs to embrace the arrival of the Metaverse era in a positive way and use the most advanced information technology to realize the revolution of learning. To stimulate excellent character of the young generation in which innovation, creation, entrepreneurial ability and love for the motherland.
Distance-Based Delays in Echo State Networks

Physical reservoir computing, a paradigm bearing the promise of energy-efficient high-performance computing, has raised much attention in recent years. We argue though, that the effect of signal propagation delay on reservoir task performance, one of the most central aspects of physical reservoirs, is still insufficiently understood in a more general learning context. Such physically imposed delay has been found to play a crucial role in some specific physical realizations, such as integrated photonic reservoirs. While delays at the readout layer and input of Echo State Networks (ESNs) have been successfully exploited before to improve performance, to our knowledge this feature has not been studied in a more general setting. We introduce inter-node delays, based on physical distances, into ESNs as model systems for physical reservoir computing. We propose a novel ESN design that includes variable signal delays along the connections between neurons, comparable to varying axon lengths in biological neural networks or varying length delay lines in physical systems. We study the impact of the resulting variable inter-node delays in this setup in comparison with conventional ESNs and find that incorporating variable delays significantly improves reservoir performance on the NARMA-10 benchmark task.
“Metaverse” is a New ICT Blockbuster

Computers are an essential part of our daily lives. For many people, computers are not only a convenient means of work but an integral part of the concept of “life”. Metaverse is the hottest innovation in the ICT society that has been offered for development in the last decade. It will undoubtedly take some time for users to use VR collaborative platform as regularly as they do now. Undoubtedly, a new culture of life will emerge. There will be ups and downs, but the goal is worth it. The presented paper describes the prerequisites and the readiness of the ICT community to participate in the project “Metaverse”.
Design-Space Exploration of Quantized Transposed Convolutional Neural Networks for FPGA-based Systems-on-Chip

With the shift of deep learning applications to Edge Computing devices, compression techniques have been introduced to minimize hardware use, power consumption and latency. For example, quantization uses low numeric precision to represent inputs, parameters and activation functions. Transposed Convolutions (TCONVs) provide neural networks with image up-sampling capabilities. However, the accuracy and performance trade-off of TCONV Layers is under-explored, with existing works evaluating down to 8-bit precision but not less. This research systematically evaluates the impact of very low precision when a two-layers quantized decoder, using TCONVs, is implemented within an FPGA-based System-on-Chip (SoC) architecture. We evaluate the quantization impact on throughput performance and hardware costs, as well as the impact of parallelizing the computations of TCONV Layers using the same metrics. Results show that, when 4-bit data are processed, the circuit implemented on a Xilinx Zynq-7020 SoC only uses ~15% of logic and ~7.5% of on-chip memories, at the expense of a negligible ~2.5% accuracy loss with respect to the 8-bit counterpart. Furthermore, 3.5× speed-up is observed when inputs are processed with 4× parallelism.
Research and Design of Human Behavior Recognition Method in Industrial Production Based on Depth Image

Correct assembly behavior in industrial production is a direct means to ensure production quality and efficiency. Aiming at the problems of worker misoperation or lack of important assembly steps in the production and assembly process, a human behavior recognition method based on ToF camera is proposed. The method segments the extracted depth motion maps (DMMs) according to the differences between frames, and extracts histogram of oriented gradient (HOG) descriptors and multiscale grayscale count (MGC) descriptors as local features. Then a hierarchical DMMs multi-classifier recognition framework is built based on stacking strategy, combining support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF) and XGBoost classifiers, achieving 98.2% accuracy on MSR ACTION 3D dataset and 87.1% accuracy on self-built dataset, respectively.
Classification of Issue Discussions in Open Source Projects Using Deep Language Models

Open source projects are supported by contributors located in different locations and time zones. Issues facilitate asynchronous communication among them, including bug reports, solution suggestions, questions, answers or personal communication. The discussion threads contain rich information about the requirements and the system problems, yet they may be lengthy and lack of structure; making it challenging to identify and link the relevant parts to requirements. Previous work addresses this problem by applying shallow machine learning techniques to automatically classify sentences in a discussion. We propose using the state-of-the-art transformer-based deep language models for this task. The contribution of this paper is three-fold. First, we create a benchmark for the community to ensure standardized inputs for training and testing for this problem. Second, we replicate the state-of-the-art and report the results on this benchmark. Third, we significantly outperform the state-of-the-art with our transformed-based classification approach.
Instant Architecture in Minecraft using Box-Split Grammars

In this paper, we present a formalism we call Box-Split Grammars for the procedural modeling of structures in Minecraft and similar environments. Our grammars are based on previous work on split grammars and box grammars, where rules define how a given box, labeled with a non-terminal symbol, can be split into smaller pieces, and how subsequent rules are to be applied. We represent grammar rules as ordinary, well-structured python functions, allowing the integration into existing systems, and demonstrate their utility by recreating variations of ancient Greek temples using a few simple grammar rules.
A Privacy-Preserving Deep Learning Scheme for Edge-enhanced Smart Homes

Smart cities make life easier for people. The smart home is an important part of smart cities, and is a typical human-cyber-physical system (HCPS). The application of AI techniques in smart homes requires cloud servers for deep model training, involving the risk of privacy leakage while data transmission to the cloud servers. The data privacy leakage in cyberspace potentially threatens the physical world. The smart home intelligent network gateway is suitable and competent to serve as an edge server. We propose a novel deep model training scheme. Introducing edge computing enables local deep models training, and potentially sensitive local data does not have to leave a trusted environment to avoid the risk of privacy leaks. With the collaboration of edge servers and cloud servers, deep model training is conducted in a federated learning manner. The implementation of privacy-preserving deep model training ensures the application of AI technology in the smart home environment. We have verified the feasibility of the scheme by experiments.
Object Detection for Autonomous Vehicle in Hazy Environment using Optimized Deep Learning Techniques

Object detection and lane detection have significant impact on consumers as they are important part of systems related to surveillance and security. It involves detecting the objects which include vehicles, humans, buildings, traffic lights, stray animals, lanes etc. In development of Autonomous driving, object detection plays an important rule. Major challenge to the implementation of idea of autonomous driving is to build systems which are secure, robust and trusted in driving environment. For empowering self-governed vehicles to work effectively in hazy or foggy environment, its systems must accurately determine objects such cars or persons etc in foggy, hazy or dusty weather. Real-time determining of objects by object detectors is done using some deep learning techniques from which YOLOv3 is prominent one.
A Comprehensive Study of the Basics of Quantum Networks

Quantum networks are the latest networks that have the tremendous ability to perform functions that traditional networks cannot perform, like clock synchronization and secure remote computation, and others, as these networks are subject to quantum mechanics, which is considered the power of this network, such as quantum entanglement and the theory of no-cloning and others, where many studies have been presented about the quantum networks in various aspects Such as studies on communication in quantum networks, in addition to studies that include part of the principles of the work of these networks. So this research aims to present a comprehensive study on the basics of quantum networks in order to give an overview of what these networks lie. Thus, this research was able to make a general comparison between classical networks and quantum networks, in addition to presenting the principles of work of quantum networks in a clear and sequential manner.
DCSP-Yolov5: Improved Yolov5 Based on Dilated Convolution for Object Detection of Forward-Looking Sonar Images

Underwater object detection based on forward-looking sonar (FLS) images has been a popular topic in recent years and numerous detection paradigms designed and optimized for optical images have been applied to FLS images. However, most of these detection paradigms are hard to achieve top performance. In this paper, we propose the Cross Stage Partial Block (DCSPBlock) to build backbone network, which is called DCSP-chain. Specifically, DCSPBlock consists of Res(1) Block and skip-layer with dilated convolution (SLDC). Therefore, the DCSP-chain can reduce redundant gradient information and provide effective receptive fields. Additionally, the BiFPN with 4 levels serves as multi-scale feature fusion network to increase FLS detection accuracy. Benefiting from DCSP-chain and multi-scale feature fusion, the proposed DCSP-YOLOv5 paradigm significantly improves the perception ability of objects. The heat map further visualizes the attention of DCSP-YOLOv5 to FLS image. Furthermore, numerous experiments show that the proposed paradigm achieves competitive performance on URPC2021 FLS dataset.
Multi-level Fusion of Fisher Vector Encoded BERT and Wav2vec 2.0 Embeddings for Native Language Identification

Native Language Identification is a prominent paralinguistic study with applications ranging from biometric analysis to speaker adaptation. Former studies on this task have benefited from alternative acoustic feature representations and pre-trained neural networks. In this work, we explore the Native Language Identification performance of contextual acoustic (wav2vec 2.0) and linguistic (BERT) embeddings as state-of-the-art feature representations and combine them with acoustic features at different levels. We encode acoustic and linguistic features using Fisher Vectors, applying Fisher Vector encoding on BERT word embeddings and wav2vec 2.0 for the first time for a paralinguistic task. We compare this approach with conventional functional summarization. In line with our former study using only acoustic modality, the results indicate the superiority of Fisher Vectors encoding over the traditional techniques. Moreover, we show the efficacy of combining alternative representations now in both acoustic and linguistic modalities. Results indicate a notable contribution of the transformer-based contextual auditory and linguistic feature representations to bimodal Native Language Identification systems.
A Spectral Analysis of Light Field Signal for Plenoptic Sampling and Rendering

Light field rendering (LFR) has been widely used for generating multiview images in Image-Based Rendering. However, to ensure the quality of novel views, this conventional rendering technology, requires a mass of manually captured light field images as input, which contains complete light field signal information and is highly time-consuming. Depth information of 3D scene and light field spectrum analysis are fundamental to light field reconstruction. The depth information was acquired with spectrum statistical analysis. This study aims to provide a broad applicability and robust depth layered scene mapping method by extending the traditional plenoptic sampling theory to reasonably determine the number of necessary captured images. A mathematical function was derived on the basis of overlapped spectrum, to decide the optimal mapping layer. Then, an optimal reconstruction filter is designed to reconstruct novel views. The proposed method exhibited a larger PSNR value in a variety of scenes. The depth layered scene mapping method showed potential for light field rendering without the need for complete multiview images requiring continuous sampling.
REFLECTION OF VIRTUAL REALITY ON ACCOUNTING EDUCATION: TRANSFORMATION OF UNIVERSITY TO METAVERSITY

The constraints created by the COVID-19 pandemic have made face-to-face communication very difficult, leading to the transfer of activities that were previously thought to be only available offline to virtual environments. Metaverse, a fictional universe composed of all digital worlds and a virtual sharing space, is not a new phenomenon. However, as a result of the continuation of education in virtual environments during the pandemic period, its entry into our lives has accelerated and the integration of education with Metaverse has begun to be discussed. In this study, the usability of Metaverse for educational purposes in universities is discussed. In this context, the opportunities offered by conducting accounting courses in a three-dimensional and interactive environment and the limits of this technology are revealed.
Optimal and Risk-Aware Path Planning considering Localization Uncertainty for Space Exploration Rovers

The reliability of autonomous traverses of rovers is critical. It may be jeopardized by the accumulation of errors and the uncertainty propagation of their localization systems. Moreover, space environments are usually harsh, challenging and unpredictable. Teleoperation is complex due to the significant and unavoidable delay. For these reasons, a path planner that provides some level of autonomy with guarantees could increase the success rate of planetary exploration missions. This paper proposes a path planning solution that tackles increasing localization uncertainty and makes a trade-off between the collision risk and the path length. The planner uses the the Fast Marching Method (FMM) to produce a costmap aware of this uncertainty and calculate the optimal path for a level of confidence. This paper additionally presents several simulation and experimental using a wheeled robotic vehicle within a lunar analogue facility.
Robust Federated Learning Over Noisy Fading Channels

The performance capabilities of models trained in a federated learning (FL) setting over wireless networks can be significantly affected by the underlying properties of the transmission channel. Even for shallow models, there can be an acute degradation in performance which necessitates the development of algorithms which are robust to transmission channel effects, such as noise and fading. In this work, we present a two-pronged approach to overcome the limitations of existing wireless machine learning (ML)-based algorithms. First, to tackle the effect of channel noise, we incorporate a novel tracking-based stochastic approximation scheme in the standard federated averaging pipeline which averages out the effect of the channel noise. In contrast to previous works on FL with a noisy channel, we provide exact convergence guarantees for our algorithm without the need to increase the transmission power gain. Second, to combat channel fading and further optimize the power consumption at the client level, we propose an adaptive transmission policy obtained by solving an optimization problem with long-term constraints. The solution is obtained in an online manner via a dual decomposition method. The superior empirical performance of the proposed scheme compared to state-of-the-art works is demonstrated on standard ML tasks.
Improving Deep Learning Based Second-Order Side-Channel Analysis With Bilinear CNN

In recent years, deep learning techniques have received significant attention in the side-channel community due to their state-of-the-art performance in profiled attacks against embedded devices. Compared with template attacks, deep learning-based attacks can deal with the high dimensionality of trace and misalignment without pre-processing. However, the performance of attacks is very sensitive to the network architecture, especially when considering masking countermeasures. Although previous works have shown the potential of neural networks to break the first-order masking, the inner-working of how the network combines the leakage of mask and masked value is still unclear and could be suboptimal. To reduce this gap, we propose to embed product combination, which has been proved to be the best combination function in noisy situations, into the design of neural networks. To this end, we introduce a bilinear convolutional neural network (in short, B-CNN) for efficient profiled attacks against the widely used masking countermeasure. In order to interpret the inner-working and decision-making of B-CNN, we propose a new visualization tool called layer-wise correlation that can reveal the points of interest and help to understand the combination of leakages. We evaluate our networks on several public datasets, e.g., ASCAD and CHES CTF 2018. The results indicate that B-CNN converges significantly faster than classic CNN models, even using a very limited number of profiling traces (e.g., 8000 profiling traces for the ASCAD dataset). Moreover, our networks perform even systematically better (w.r.t. the number of attack traces) than the current state-of-the-art results on all the investigated datasets.
A Game Theoretical Vulnerability Analysis of Adversarial Attack

In recent times deep learning has been widely used for automating various security tasks in Cyber Domains. However, adversaries manipulate data in many situations and diminish the deployed deep learning model's accuracy. One notable example is fooling CAPTCHA data to access the CAPTCHA-based Classifier leading to the critical system being vulnerable to cybersecurity attacks. To alleviate this, we propose a computational framework of game theory to analyze the CAPTCHA-based Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We apply the Fast Gradient Symbol Method (FGSM) and One Pixel Attack on CAPTCHA Data to imitate real-life scenarios of possible cyber-attack. Subsequently, to interpret this scenario from a Game theoretical perspective, we represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies in the game theory prospect.
Multitask Semantic Segmentation Network Using Adaptive Multiscale Feature Fusion

A multi-task semantic segmentation network architecture based on adaptive multi-scale feature fusion is proposed, which improves segmentation target edge details and small-scale target segmentation accuracy by combining boundary detection tasks and semantic segmentation tasks. The critical component of the architecture is the adaptive multi-scale feature fusion module, which can adaptively fuse the semantic feature information and boundary feature information of different scales, extract semantic features that contain more spatial data, and reduce the loss of spatial information of small-scale targets, thereby enhancing the network's ability to learn small-scale target features and boundary features. Experiments show that our designed network architecture can improve the segmentation accuracy of small-scale objects and optimize the edge details of segmented objects.