Reviewerly/exHarmony huggingface dataset:
Two-Level Attention Module Based on Spurious-3D Residual Networks for Human Action Recognition

In recent years, deep learning techniques have excelled in video action recognition. However, currently commonly used video action recognition models minimize the importance of different video frames and spatial regions within some specific frames when performing action recognition, which makes it difficult for the models to adequately extract spatiotemporal features from the video data. In this paper, an action recognition method based on improved residual convolutional neural networks (CNNs) for video frames and spatial attention modules is proposed to address this problem. The network can guide what and where to emphasize or suppress with essentially little computational cost using the video frame attention module and the spatial attention module. It also employs a two-level attention module to emphasize feature information along the temporal and spatial dimensions, respectively, highlighting the more important frames in the overall video sequence and the more important spatial regions in some specific frames. Specifically, we create the video frame and spatial attention map by successively adding the video frame attention module and the spatial attention module to aggregate the spatial and temporal dimensions of the intermediate feature maps of the CNNs to obtain different feature descriptors, thus directing the network to focus more on important video frames and more contributing spatial regions. The experimental results further show that the network performs well on the UCF-101 and HMDB-51 datasets.
Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

We introduce<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:math>, an open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its Python front-end allows photonic circuits to be composed from basic photonic building blocks like photon sources, beam splitters, phase-shifters and detectors. A variety of computational back-ends are available and optimised for different use-cases. These use state-of-the-art simulation techniques covering both weak simulation, or sampling, and strong simulation. We give examples of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:math>in action by reproducing a variety of photonic experiments and simulating photonic implementations of a range of quantum algorithms, from Grover's and Shor's to examples of quantum machine learning.<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:math>is intended to be a useful toolkit for experimentalists wishing to easily model, design, simulate, or optimise a discrete-variable photonic experiment, for theoreticians wishing to design algorithms and applications for discrete-variable photonic quantum computing platforms, and for application designers wishing to evaluate algorithms on available state-of-the-art photonic quantum computers.
Exploring the Top Five Evolving Threats in Cybersecurity: An In-Depth Overview

The term cybersecurity refers to an environment capable of protecting digital devices, networks and information from unauthorized access and preventing data theft or alteration. It is composed of a collection of carefully crafted techniques, processes, and practices to protect sensitive information and deterring cyber-attacks. In the recent period, the domain of cybersecurity has undergone rapid growth in response to the increasing cyber threats. Cybersecurity includes important tactics that help protect the digital environment, which are firewalls, encryption, secure passwords, and threat detection and response systems. Employees must be trained on these tactics. This article will discuss the five most pressing challenges facing the cybersecurity industry today that must be taken into account by businesses, organizations, and individuals in order to secure their confidential data from cybercrime. The conclusion of the article highlighted the significance of growing awareness about cybersecurity risks in order to effectively handle digital environments and protect them from any electronic threats.
Supersingular Curves You Can Trust

Generating a supersingular elliptic curve such that nobody knows its endomorphism ring is a notoriously hard task, despite several isogeny-based protocols relying on such an object. A trusted setup is often proposed as a workaround, but several aspects remain unclear. In this work, we develop the tools necessary to practically run such a distributed trusted-setup ceremony. Our key contribution is the first statistically zero-knowledge proof of isogeny knowledge that is compatible with any base field. To prove statistical ZK, we introduce isogeny graphs with Borel level structure and prove they have the Ramanujan property. Then, we analyze the security of a distributed trusted-setup protocol based on our ZK proof in the simplified universal composability framework. Lastly, we develop an optimized implementation of the ZK proof, and we propose a strategy to concretely deploy the trusted-setup protocol.
Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

The aim of the peer-to-peer (P2P) decentralized gaming industry has shifted towards realistic gaming environment (GE) support for game players (GPs). Recent innovations in the metaverse have motivated the gaming industry to look beyond augmented reality and virtual reality engines, which improve the reality of virtual game worlds. In gaming metaverses (GMs), GPs can play, socialize, and trade virtual objects in the GE. On game servers (GSs), the collected GM data are analyzed by artificial intelligence models to personalize the GE according to the GP. However, communication with GSs suffers from high-end latency, bandwidth concerns, and issues regarding the security and privacy of GP data, which pose a severe threat to the emerging GM landscape. Thus, we proposed a scheme, Game-o-Meta, that integrates federated learning in the GE, with GP data being trained on local devices only. We envisioned the GE over a sixth-generation tactile internet service to address the bandwidth and latency issues and assure real-time haptic control. In the GM, the GP's game tasks are collected and trained on the GS, and then a pre-trained model is downloaded by the GP, which is trained using local data. The proposed scheme was compared against traditional schemes based on parameters such as GP task offloading, GP avatar rendering latency, and GS availability. The results indicated the viability of the proposed scheme.
Controlling Two-Dimensional Coulomb Crystals of More Than 100 Ions in a Monolithic Radio-Frequency Trap

Linear strings of trapped atomic ions held in radio-frequency (rf) traps constitute one of the leading platforms for quantum simulation experiments, allowing the investigation of interacting quantum matter. However, linear ion strings have drawbacks, such as the difficulty of scaling beyond approximately 50 particles as well as the inability to naturally implement spin models with more than one spatial dimension. Here we present experiments with planar Coulomb crystals of up to 105 40Ca+ ions in a novel monolithic rf trap, laying the groundwork for quantum simulations of two-dimensional spin models with single-particle control. We characterize the trapping potential by analysis of crystal images and compare the observed crystal configurations with numerical simulations. We further demonstrate stable confinement of large crystals, free of structural configuration changes, and find that rf heating of the crystal is not an obstacle for future quantum simulation experiments. Finally, we prepare the out-of-plane motional modes of planar crystals consisting of up to 105 ions close to their ground state by electromagnetically induced transparency cooling, an important prerequisite for implementing long-range entangling interactions.Received 2 February 2023Accepted 6 April 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020317Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum simulationPhysical SystemsTrapped ionsTechniquesCooling & trappingQuantum InformationAtomic, Molecular & Optical
A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art network of object detection, YOLOv5, has been widely used on insulator and defect detection. However, YOLOv5 network has some limitations like poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced Ghost module into YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance in unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention module (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) 0.5 and the mAP0.5:0.95 of our model can reach 99.4% and 91.7%, the parameters and model weight are reduced to 3807372 and 8.79M, which can easily deploy to embedded devices like UAVs. And the speed of detection can reach 10.9ms/image, which can meet the real-time detection requirement.
AI-writing tools in education: if you can’t beat them, join them

Abstract The release and rapid diffusion of ChatGPT has forced teachers and researchers around the world to grapple with the consequences of artificial intelligence (AI) for education. For second language educators, AI-generated writing tools such as ChatGPT present special challenges that must be addressed to better support learners. We propose a five-part pedagogical framework that seeks to support second language learners through acknowledging both the immediate and long-term contexts in which we must teach students about these tools: understand, access, prompt, corroborate, and incorporate. By teaching our students how to effectively partner with AI, we can better prepare them for the changing landscape of technology use in the world beyond the classroom.
Analyzing the User's Sentiments of ChatGPT Using Twitter Data

ChatGPT, an advanced language model based on artificial intelligence developed by the OpenAI, was released to internet users on November 30, 2022, and has attracted a great deal of attention. The feelings and thoughts of those who first experienced ChatGPT are valuable feedback for evaluating the success and positive and negative aspects of this technology. In this study, sentiment analysis of ChatGPT-themed tweets on Twitter was conducted to comprehensively evaluate the feelings and thoughts of users during the first two months following the announcement of ChatGPT. Approximately 788.000 English tweets were analyzed using the AFINN, Bing, and NRC sentiment dictionaries. The findings indicate that a large portion of the initial users of ChatGPT found the experience to be successful and was satisfied with ChatGPT. However, negative emotions such as fear and concern were also observed in some users. This study presents the most comprehensive sentiment analysis on ChatGPT. In future studies, specialized research can be conducted on the performance of ChatGPT in a specific field.
Dual Teacher: A Semisupervised Cotraining Framework for Cross-Domain Ship Detection

Cross-domain ship detection tries to identify Synthetic Aperture Radar (SAR) ship by adapting knowledge from labeled optical images, without labor-intensive annotations. In practical applications, a few ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., one or three samples) labeled SAR samples are available, which provides an additional supervision for SAR ships. However, the existing cross-domain methods ignore the SAR supervision (a few labeled and unlabeled SAR images), which limits their performances in a practical and under-investigated task: semi-supervised cross-domain ship detection. In this paper, a Dual Teacher framework is proposed to address the mutual interference between the optical supervision and the SAR supervision. First, both optical and SAR supervision are decomposed into two sub-tasks: cross-domain task and semi-supervised task. Then, both cross-domain task and semi-supervised task can be learned interactively in two individual teacher-student models. The teacher-student models generate pseudo-labels on unlabeled SAR images by a teacher network and fine-tune the student network. Finally, the Dual Teacher framework retrains two teacher-student models in co-training strategies. Both cross-domain dataset and semi-supervised dataset are exploited to jointly improve the pseudo-label quality. The effectiveness of the Dual Teacher framework has been fully experimentally demonstrated. The code is available at https://github.com/XiangtaoZheng/DualTeacher.
Simple and Efficient Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) designed for homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. In this paper, we conduct an in-depth and detailed study of these mechanisms and propose the Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of a simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.
Metaverse and translation studies: analysis of machine translation

Although translation is essential, in collaboration with artificial intelligence, for the development and success of interaction and communication in the metaverse, little attention has been paid to the direct influence of linguistic communication in three-dimensional space. For this reason, in this paper, we have analyzed different investigations focused on the metaverse, coming from various disciplines using the machine translation service. The purpose is to know the perceptions about its use and quality and thus understand its potential in virtual reality and challenges. Documents from the SciELO, Google Scholar, and Dialnet databases were retrieved. Those studies that have directly or indirectly used the machine translation tool to favor communication and interaction between users were included. The results show a positive general perception by the studies analyzed, although the imprecision of the automatic translation engines and the lack of language combinations are qualified. Lastly, the importance of studying communication in the metaverse is highlighted, opening up the number of available languages and researching the training of translation engines since the income from the machine translation market is destined to increase in the coming years because of the pace of manufacturing and the productivity offered.
Novel Motion Patterns Matter for Practical Skeleton-Based Action Recognition

Most skeleton-based action recognition methods assume that the same type of action samples in the training set and the test set share similar motion patterns. However, action samples in real scenarios usually contain novel motion patterns which are not involved in the training set. As it is laborious to collect sufficient training samples to enumerate various types of novel motion patterns, this paper presents a practical skeleton-based action recognition task where the training set contains common motion patterns of action samples and the test set contains action samples that suffer from novel motion patterns. For this task, we present a Mask Graph Convolutional Network (Mask-GCN) to focus on learning action-specific skeleton joints that mainly convey action information meanwhile masking action-agnostic skeleton joints that convey rare action information and suffer more from novel motion patterns. Specifically, we design a policy network to learn layer-wise body masks to construct masked adjacency matrices, which guide a GCN-based backbone to learn stable yet informative action features from dynamic graph structure. Extensive experiments on our newly collected dataset verify that Mask-GCN outperforms most GCN-based methods when testing with various novel motion patterns.
Intriguing Findings of Frequency Selection for Image Deblurring

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference between blurry and sharp image pairs from pixel-level, which inevitably overlooks the importance of blur kernels. This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). Based on this observation, we attempt to leverage kernel-level information for image deblurring networks by inserting Fourier transform, ReLU operation, and inverse Fourier transform to the standard ResBlock. 1 × 1 convolution is further added to let the network modulate flexible thresholds for frequency selection. We term our newly built block as Res FFT-ReLU Block, which takes advantages of both kernel-level and pixel-level features via learning frequency-spatial dual-domain representations. Extensive experiments are conducted to acquire a thorough analysis on the insights of the method. Moreover, after plugging the proposed block into NAFNet, we can achieve 33.85 dB in PSNR on GoPro dataset. Our method noticeably improves backbone architectures without introducing many parameters, while maintaining low computational complexity. Code is available at https://github.com/DeepMed-Lab/DeepRFT-AAAI2023.
Artificial Intelligence (AI) in the Hospitality Industry: A Review Article

Purpose -Artificial intelligence (AI) adoption is critical in the age of digital technology.This review article aims to evaluate the literature on AI in the hospitality industry.Method -A narrative synthesis was used in this review article.Moreover, the literature was reviewed systematically to explore AI in the hospitality industry.The literature and information were obtained from various books and research articles on EBSCO, Google Scholar, Scopus, Web of Science, and ScienceDirect.The inclusion criteria were studies that clearly defined AI in all aspects of the hospitality industry, were published and written in English and were peer-reviewed.Content analysis was employed.Results -The use of AI is a strategic and critical factor in economic development.Furthermore, AI technologies are increasingly being used as digital assistants.They help businesses in the hospitality industry in a variety of ways, including improving customer service, expanding operational capability, and lowering costs.However, there are some risks associated with AI advancements, such as job loss in low-tech sectors, loss of control due to robot autonomy, and safety, security, and privacy concerns. 1307Conclusion -AI technologies have both positive and negative effects on the workforce and job employment in the hospitality industry.Recommendations -The recommendation is to consider a quantitative study regarding AI adoption in the hospitality industry or other sectors.Also, a qualitative approach could give a clear view of insight results for further study.Research Implications -This review article contributed to the existing literature on AI adoption in the hospitality industry.Hence, it could be used to guide future research on AI adoption in the hospitality industry.It may also aid academics in broadening their research by incorporating more potential elements.Practical Implications -This review article could lead to a better understanding of AI adoption in the hospitality industry.Moreover, it may assist business owners, managers, and marketers in the hospitality industry or any sector to achieve and enhance high business performance by implementing appropriate strategies to meet the needs and expectations of both customers and employees through the use of AI.
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of ML training data. Differential Privacy (DP) has become a gold standard for making formal statements about data anonymization. However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between. The adoption of DP is hindered by limited practical guidance of what DP protection entails, what privacy guarantees to aim for, and the difficulty of achieving good privacy-utility-computation trade-offs for ML models. Tricks for tuning and maximizing performance are scattered among papers or stored in the heads of practitioners. Furthermore, the literature seems to present conflicting evidence on how and whether to apply architectural adjustments and which components are "safe" to use with DP. This work is a self-contained guide that gives an in-depth overview of the field of DP ML and presents information about achieving the best possible DP ML model with rigorous privacy guarantees. Our target audience is both researchers and practitioners. Researchers interested in DP for ML will benefit from a clear overview of current advances and areas for improvement. We include theory-focused sections that highlight important topics such as privacy accounting and its assumptions, and convergence. For a practitioner, we provide a background in DP theory and a clear step-by-step guide for choosing an appropriate privacy definition and approach, implementing DP training, potentially updating the model architecture, and tuning hyperparameters. For both researchers and practitioners, consistently and fully reporting privacy guarantees is critical, and so we propose a set of specific best practices for stating guarantees.