Reviewerly/exHarmony huggingface dataset:
Hybrid Algorithm for Real-Time Sign Language Detection System

This research paper suggests a machine learning-based system designed to interpret sign languages in real-time from a video feed to help deaf and hard of hearing individuals overcome communication barriers. Sign languages employ hand gestures, facial expressions, and body language to convey ideas and meaning visually. Unfortunately, individuals with hearing impairments face communication difficulties, limited access to information and services, and social isolation. The proposed system captures the subject's gestures using a camera, which are then processed using a TensorFlow object detection API that predicts based on a pre-trained machine learning model. The system's methodology incorporates supervised learning, Single Shot MultiBox Detector (SSD) object detection algorithm, and the luminosity method for converting colour images to grayscale. For dataset creation, hand gesture image samples were captured and labelled using open-source graphical annotation software LabelImg, and the model was trained. The system proposed achieved an average accuracy of 95% and has the potential to improve communication and reduce marginalization for deaf and hard of hearing individuals.
Human Pose Recommendation and Professionalization

Thanks to the proliferation of smartphones, taking photos is a breeze. Embarrassingly, we often find it difficult to strike a proper pose due to a lack of professional photography knowledge or guidance. The resulting photos are less than satisfactory. Nowadays, there exist plenty of scenarios where the original person images need to be automatically modified, such as social-media apps and photo-sharing sites. To solve this problem, we introduce a two-stage framework for human pose recommendation and professionalization. This novel pose-guided person image generation task is to transform a source person image to a professional pose. First, the Pose Recommendation Stage, then Reposing Stage. In the recommendation stage, a dataset of professional person pose images (Human Posture Recommendation Templates, HPRT) is first collected. And we propose human posture recommendation algorithm. Given a source person image, our algorithm can find some proper reference posture images in this shooting scene from the collected template dataset. Then, in reposing stage, we propose a pose-conditioned transformer-based StyleGAN generator to translate the source person image to the reference posture. Also, to make the image more realistic, we add completion operation. So our work can automatic modify the person pose in the image with more professional one without any manual operations. Qualitative and quantitative evaluations show that our two-stage framework can solve such task well.
Research on Federated Learning Security Defense Technology

In a federated learning environment, data interaction and sharing between nodes are inevitable. Malicious attackers may tamper with the data of certain nodes or even train models, posing a security threat to entities. Among these security attacks, backdoor attacks are the most covert. On the basis of existing research, this article designs a federated learning convergence wheel backdoor defense method and applies it to the field of vehicle networking. This method can effectively defend against backdoor attacks.
English Teaching Ability Evaluation Algorithm based on K-Means Data Algorithm

In recent years, with the introduction of modern technology and algorithms, major changes have taken place in the field of English teaching in order to evaluate teachers' abilities. One of the algorithms is K-means data algorithm, which has been widely studied to evaluate English teaching ability. The research of this algorithm involves the comprehensive analysis of various factors that affect teachers' performance, including their teaching skills, knowledge, experience and communication ability. K-means data algorithm uses machine learning technology to analyze these factors and generate accurate evaluation based on real-time data. The benefits of this study are many. First, it provides an objective evaluation system that eliminates any subjective bias or personal opinions. Secondly, it enables teachers to identify their strengths and weaknesses and strive to improve their abilities.
2CET-GAN: Pixel-Level GAN Model for Human Facial Expression Transfer

Recent studies have used GANs to transfer expressions between human faces. However, existing models have some flaws, such as relying on emotion labels, lacking continuous expressions, and fail- ing to capture the expression details. To address these limitations, we propose a novel two-cycle network called 2 Cycles Expression Transfer GAN (2CET-GAN), which can learn continuous expression transfer without using emotion labels in an unsupervised fashion. The proposed network learns the transfer between two distribu- tions while preserving identity information. The quantitative and qualitative experiments on two public datasets of emotions (CFEE and RafD) show our network can generate diverse and high-quality expressions and can generalize to unknown identities. We also com- pare our methods with other GAN models and show the proposed model generates expressions that are closer to the real distribution and discuss the findings. To the best of our knowledge, we are among the first to successfully use an unsupervised approach to disentangle expression representation from identities at the pixel level. Our code is available at github.com/xiaohanghu/2CET-GAN.
Aerosol spatio-temporal evolution map feature finding comparison method based on image similarity algorithm

In this paper, a method based on different similarity algorithms is proposed to find and compare the salient features in the spatio-temporal evolution map of aerosols. The pixel number difference of similar features calculated using this method in different aerosol spatio-temporal evolution maps is equivalent to the movement time. By analyzing the design principles, advantages and disadvantages of four different algorithms: SSIM, Corr2, Ahash and Dhash, the simulation analysis experiment is designed. From the aspects of theoretical analysis and experimental test, the images with different pixel numbers are layered and solved, and the solution effects of different similarity algorithms are comprehensively compared. The results of simulation and comparison show that among the four similarity algorithms, Ahash and Dhash algorithms perform well and have less fluctuation in accuracy, and the above conclusions can provide reference for the feature finding comparison methods of similarity algorithms in aerosol density mapping.
Utilizing Large Language Models for the Generation of Aspect-Based Sentiment Analysis Datasets

With the rapid development of the field of Aspect-Based Sentiment Analysis (ABSA), the importance of Data Set(DS)labeling for training high-quality ABSA models has become increasingly prominent. In the past, the ABSA DS labeling task was usually handed over to crowd workers on the MTurk platform or expert classifiers, but with the development of Large Language Models(LLMs) in natural Language generation tasks, it shows great potential in data labeling. In this paper, we propose a sentiment analysis framework that automatically acquires labeled MOOC’s DS to evaluate the accuracy, reliability, and bias of LLMs in ABSA text analysis tasks compared to manual ones. The results show that ChatGPT is superior to humans in ABSA labeling tasks, the zero-sample accuracy and code-to-code consistency both exceed crowdsourced workers. These findings show that LLMs do have the potential to be used for dataset labeling tasks, and can provide a new feasible idea for future DS labeling research in the ABSA field.
Classifying space-time images obtained from distributed acoustic sensing

In this paper we present a classifier that was trained on space-time images obtained from distributed acoustic sensing, for the purpose of monitoring earthquakes. The model is capable of discriminating between actual and non-earthquake events.
A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection

Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP@0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices.
Typical Fault Detection on Drone Images of Transmission Lines Based on Lightweight Structure and Feature-Balanced Network

In the context of difficulty in detection problems and the limited computing resources of various fault scales in aerial images of transmission line UAV inspections, this paper proposes a TD-YOLO algorithm (YOLO for transmission detection). Firstly, the Ghost module is used to lighten the model’s feature extraction network and prediction network, significantly reducing the number of parameters and the computational effort of the model. Secondly, the spatial and channel attention mechanism scSE (concurrent spatial and channel squeeze and channel excitation) is embedded into the feature fusion network, with PA-Net (path aggregation network) to construct a feature-balanced network, using channel weights and spatial weights as guides to achieving the balancing of multi-level and multi-scale features in the network, significantly improving the detection capability under the coexistence of multiple targets of different categories. Thirdly, a loss function, NWD (normalized Wasserstein distance), is introduced to enhance the detection of small targets, and the fusion ratio of NWD and CIoU is optimized to further compensate for the loss of accuracy caused by the lightweightedness of the model. Finally, a typical fault dataset of transmission lines is built using UAV inspection images for training and testing. The experimental results show that the TD-YOLO algorithm proposed in this article compresses 74.79% of the number of parameters and 66.92% of the calculation amount compared to YOLOv7-Tiny and increases the mAP (mean average precision) by 0.71%. The TD-YOLO was deployed into Jetson Xavier NX to simulate the UAV inspection process and was run at 23.5 FPS with good results. This study offers a reference for power line inspection and provides a possible way to deploy edge computing devices on unmanned aerial vehicles.
Modulation variance optimization in discrete modulated CV-QKD systems

The distribution of symmetric keys over optical networks is possible through the implementation of continuous variables quantum key distribution (CV-QKD) systems. Despite the rate of key extraction in CV-QKD being maximized for Gaussian modulation (GM), its practical implementation is demanding due to the finite extinction ratio of the electro-optic modulators. The use of discrete modulation (DM) allows for a simpler practical implementation. Provided that higher-order constellations can be used, DM can approximate the performances of GM. Higher-order DM has been shown to be feasible experimentally in CV-QKD. Notwithstanding, such implementations are usually obtained in offline without applying all the post-processing steps, particularly the information reconciliation, turning the validity of the results dependent on a null frame error rate after information reconciliation. Besides that, a fixed value is typically assumed for the reconciliation efficiency value, being the modulation variance optimized under such assumptions. This disregarding the dependency of the reconciliation efficiency parameter on the method for information reconciliation and on the modulation variance. In this work we study the impact that such assumptions have on the optimization of the modulation variance in a CV-QKD system using probabilistic shaped higher-order DM. Moreover, we analyse how optimizing the modulation variance can affect the extraction key rates of symmetric keys at the output of the CV-QKD system. Besides the assumptions of null frame error rate and of the reconciliation efficiency, we study how different code rates may also affect the optimization of the modulation variance. This considering M-symbol quadrature amplitude modulation and multidimensional reconciliation.
Research on construction and management strategy of carbon neutral stadiums based on CNN-QRLSTM model combined with dynamic attention mechanism

Introduction Large-scale construction projects such as sports stadiums are known for their significant energy consumption and carbon emissions, raising concerns about sustainability. This study addresses the pressing issue of developing carbon-neutral stadiums by proposing an integrated approach that leverages advanced convolutional neural networks (CNN) and quasi-recurrent long short-term memory (QRLSTM) models, combined with dynamic attention mechanisms. Methods The proposed approach employs the CNN-QRLSTM model, which combines the strengths of CNN and QRLSTM to handle both image and sequential data. Additionally, dynamic attention mechanisms are integrated to adaptively adjust attention weights based on varying situations, enhancing the model's ability to capture relevant information accurately. Results Experiments were conducted using four datasets: EnergyPlus, ASHRAE, CBECS, and UCl. The results demonstrated the superiority of the proposed model compared to other advanced models, achieving the highest scores of 97.79% accuracy, recall rate, F1 score, and AUC. Discussion The integration of deep learning models and dynamic attention mechanisms in stadium construction and management offers a more scientific decision support system for stakeholders. This approach facilitates sustainable choices in carbon reduction and resource utilization, contributing to the development of carbon-neutral stadiums.
Dataset for cyber–physical anomaly detection in smart homes

DATA REPORT article Front. Internet Things, 17 October 2023Sec. Security, Privacy and Authentication Volume 2 - 2023 | https://doi.org/10.3389/friot.2023.1275080
Community Technology Centres as Bridges to Foster Social and Digital Equality amongst the Youth in South Africa

This article sheds some light on the role of information and communication technologies (ICTs) in promoting youth development in selected communities in KwaZulu-Natal. The study targeted the youth in four selected community technology centres in KwaZulu-Natal, South Africa. Through a survey, 207 youth were selected through convenience sampling. Data were collected through a self-administered questionnaire. In addition, data were tabulated and presented using tables, frequencies and percentiles. The study was informed by the diffusion of innovations theory. The results indicate that a variety of ICTs are available in the telecentres to provide the youth with much-needed access to information and improved communication. The study recommends that there should be sufficient and coherent government policies regulating the training of the youth to effectively use these ICTs. Government should ensure that adequate ICT training is offered to the youth. In addition, there is a need to gradually include ICT training in the school curricula at all levels, including primary education.
Searching for Swedish LGBTQI fiction: the librarians' perspective

Purpose This article aims to help ensure high-quality subject access to Swedish lesbian, gay, bisexual, transgender, queer and intersexual (LGBTQI) fiction, and aims to identify challenges that librarians consider important to address, on behalf of themselves and end users. Design/methodology/approach A web-based questionnaire comprising 35 closed and open questions, 22 of which were required, was sent via online channels in January 2022. By the survey closing date, 20 March 2022, 82 responses had been received. The study was intended to complement an earlier study targeting end users. Findings Both this study of librarians and the previous study of end users have painted a dismal image of online search services when it comes to searching for LGBTQI fiction. The need to consult different channels (e.g. social media, library catalogues and friends), the inability to search more specifically than for the broad LGBTQI category and suboptimal search interfaces were among the commonly reported issues. The results of these studies are used to inform the development of a dedicated Swedish LGBTQI fiction database with an online search interface. Originality/value The subject searching of fiction via online services is usually limited to genre with facets for time and place, while users are often seeking characteristics such as pacing, characterization, storyline, frame/setting, tone and language/style. LGBTQI fiction is even more challenging to search because indexing practices are not really being standardized or disseminated worldwide. This study helps address this important gap, in both research and practical applications.
A secure framework for <scp>IoT</scp>‐based healthcare using blockchain and <scp>IPFS</scp>

Abstract Smart healthcare, also known as IoT (Internet of Things) based healthcare, utilizes IoT technology to enhance the healthcare industry. The use of IoT‐enabled medical equipment enables remote monitoring of patients, allowing for in‐home care and alerting healthcare providers of any changes. This can lead to improved patient outcomes and better disease management. However, it is important to implement robust security measures to protect patient data when using IoT in healthcare. One potential solution is to use blockchain technology to secure data storage and sharing with medical providers. Blockchain's decentralized structure and cryptographic techniques make it difficult for hackers to access or tamper with patient information. Additionally, IPFS (InterPlanetary file system) can be used for efficient data storage and sharing with authorized medical professionals, while smart contract functionality can automate the process of granting and revoking access to patient data. This article proposes a blockchain‐based secure remote patient monitoring system, specifically for chronic disease patients. The system utilizes distributed blockchain for data security, IPFS for data storage and sharing, and DApp for data collection and connection to the blockchain, along with encryption for added security. The proposed approach is compared to existing solutions and found to be a superior option for IoT‐based healthcare.
Multimodal Interaction Grammar Analysis Based on Two-Stage User-Based Elicitation in 3D Modeling

AbstractTraditional computer-aided design (CAD) tools based on keyboard and mouse interactions present challenges to efficiency and quality in terms of free creation, iteration efficiency, and operational experience. This paper proposes a multimodal interactive guiding grammar framework for virtual-reality modeling scenarios. In studies on the characteristics of multimodal combinations, the present heuristic method leads to a high cognitive load and experimental cost, and the heuristic is difficult. Therefore, we propose a two-stage heuristic method. Subsequently, we use this method to decompose the interaction task into two stages: modalities and interactive operations. A clearer and more specific reference for multimodal selection is provided for the interaction design phase, based on the two dimensions of modality and task. This can reduce the heuristic cost and difficulty of users in completing tasks in multimodal interaction scenarios. Finally, the proposed virtual modeling platform which is used for experimental verification, and the results shows that, compared with traditional modeling, the interactive modeling method built according to the results of modal interaction in this study can better satisfy users in terms of emotional experience. In the index evaluation, high scores are achieved, with an average score of 5 points (1 and 7 are the lowest and highest scores of the evaluation index, respectively), and the large-area distribution of high scores also shows a good user experience. To a certain extent, this method improves the interaction mode of the previous 3D modeling.Keywords: Multimodal3D modelingnatural user interfacesuser-based elicitationnatural interaction AcknowledgementsTo the peers and reviewers who have provided suggestions on this paper!Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsWen-Jun HouWen-Jun Hou, born in 1964, PhD, professor. Her research interests include natural interaction, information visualization, interaction design theory and mode et al.Ge-Xin GuoGe-Xin Guo, born in 1999, PhD candidate. Her research interests include human-computer interaction, natural interaction and multimodal user interface.Yi-Ting ChengYi-Ting Cheng, born in 1997, MFA Her research interests include human-computer interaction and multimodal user interface.
How to make (mathematical) assertions with directives

Abstract It is prima facie uncontroversial that the justification of an assertion amounts to a collection of other (inferentially related) assertions. In this paper, we point at a class of assertions, i.e. mathematical assertions, that appear to systematically flout this principle. To justify a mathematical assertion (e.g. a theorem) is to provide a proof—and proofs are sequences of directives. The claim is backed up by linguistic data on the use of imperatives in proofs, and by a pragmatic analysis of theorems and their proofs. Proofs, we argue, are sequences of instructions whose performance inevitably gets one to truth. It follows that a felicitous theorem, i.e. a theorem that has been correctly proven, is a persuasive theorem. When it comes to mathematical assertions, there is no sharp distinction between illocutionary and perlocutionary success.
Smart Health Prediction Using Machine Learning

Machine learning-based smart health prediction is a fast- expanding topic that has the potential to completely change how we identify and treat diseases. We can create systems that can accurately forecast a patient's risk of contracting a disease or their chance of responding to a particular treatment by utilizing machine learning algorithms to examine enormous quantities of medical data.Although they are still in the early stages of research, smart health prediction systems could have a significant influence on how medicine is practiced in the future. Systems for making smart predictions about a person's health have the potential to boost healthcare delivery's effectiveness and quality. We can put preventative measures in place to lower a patient's risk of contracting a disease by precisely anticipating the patient's risk. Smart health prediction systems can also be used to pinpoint people who are most at risk for developing complications from an illness or who are most likely to have poor treatment outcomes. These data can be used to develop personalized treatment plans for patients.