Reviewerly/exHarmony huggingface dataset:
EarHear: Enabling the Deaf to Hear the World via Smartphone Speakers and Microphones

Sign language plays a vital role in communication and learning for individuals with hearing and speech disabilities, serving as a common language for the deaf. Current state-of-the-art sign language recognition methods primarily rely on computer vision techniques, but they have certain limitations, including susceptibility to light interference and privacy concerns. Ubiquitous acoustic sensing provides new possibilities for sign language recognition, leveraging its high resistance to interference and cost-effectiveness. However, existing methods face challenges in achieving satisfactory results due to environmental interference and the complexity of sign language recognition contexts. In this work, we propose EarHear, a robust contactless Chinese sign language recognition and translation system. EarHear adopts Differential-Doppler data pre-processing method to cleverly mitigate the interference caused by the environment. To further identify differences in the morphology, speed, and direction of sign language actions and distinguish similar gestures, we propose the Vision Transformer for Sign Language Recognition, which is able to model the context dependence of long-range features and output indeterminate long sign language sequences using an attention mechanism. As a result, computational speed and recognition accuracy are improved. Moreover, we explore a large-scale language model-based sign language translation, which enables sign language recognition results to follow natural language standards, thus realizing a true sense of sign language recognition. The evaluation results based on 15 Chinese sentences show that our system achieves an average recognition rate of 93.38% and a BLEU-1 score of 80.73% for sign language translation, reaching the most advanced level in terms of accuracy and robustness.
An Entity-Relation Joint Extraction Method Based on Two Independent Sub-Modules From Unstructured Text

Extracting entity, relation, and attribute information from unstructured text is crucial for constructing large-scale knowledge graphs(KG). Existing research approaches either focus on entity recognition before relation extraction or employ unified annotation. However, these methods overlook the intrinsic relation between entity recognition and relation extraction, resulting in ineffective handling of triple overlap issues where multiple relations share the same entity in a sentence. To address these challenges, this paper proposes an entity-relation joint extraction model comprising two independent sub-modules: one for extracting the head entity and the other for extracting the tail entity and its corresponding relation. The model generates candidate entities and relations by enumerating token sequences in sentences, and then uses the two sub-modules to predict entities and relations. The predicted entities and relations are jointly decoded to obtain relational triples, avoiding error propagation and solving redundancy, entity overlap, and poor generalization. Extensive experiments demonstrate that our model achieves state-of-the-art performance on WebNLG, NYT, WebNLG*, and NYT* public benchmarks. It outperforms all baselines on the WebNLG* dataset, showing significant improvements in different types of triples: normal, SEO, and EPO by 3.8%, 2.9%, and 5.5%, respectively, compared to ETL-Span. For the NYT* dataset, our method improves by 5.7% in triples of Normal type, thereby confirming its effectiveness.
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-Based Feature Distillation

Efficient object detection methods have recently received great attention in remote sensing. Although deep convolutional networks often have excellent detection accuracy, their deployment on resource-limited edge devices is difficult. Knowledge distillation (KD) is a strategy for addressing this issue since it makes models lightweight while maintaining accuracy. However, existing KD methods for object detection have encountered two constraints. First, they discard potentially important background information and only distill nearby foreground regions. Second, they only rely on the global context, which limits the student detector’s ability to acquire local information from the teacher detector. To address the aforementioned challenges, we propose Attention-based Feature Distillation (AFD), a new KD approach that distills both local and global information from the teacher detector. To enhance local distillation, we introduce a multi-instance attention mechanism that effectively distinguishes between background and foreground elements. This approach prompts the student detector to focus on the pertinent channels and pixels, as identified by the teacher detector. Local distillation lacks global information, thus attention global distillation is proposed to reconstruct the relationship between various pixels and pass it from teacher to student detector. The performance of AFD is evaluated on two public aerial image benchmarks, and the evaluation results demonstrate that AFD in object detection can attain the performance of other state-of-the-art models while being efficient.
Data Extraction and Question Answering on Chart Images Towards Accessibility and Data Interpretation

Graphical representations such as chart images are integral to web pages and documents. Automating data extraction from charts is possible by reverse-engineering the visualization pipeline. This study proposes a framework that automates data extraction from bar charts and integrates it with question-answering. The framework employs an object detector to recognize visual cues in the image, followed by text recognition. Mask-RCNN for plot element detection achieves a mean average precision of 95.04% at a threshold of 0.5 which decreases as the Intersection over Union (IoU) threshold increases. A contour approximation-based approach is proposed for extracting the bar coordinates, even at a higher IoU of 0.9. The textual and visual cues are associated with the legend text and preview, and the chart data is finally extracted in tabular format. We introduce an extension to the TAPAS model, called TAPAS++, by incorporating new operations and table question answering is done using TAPAS++ model. The chart summary or description is also produced in an audio format. In the future, this approach could be expanded to enable interactive question answering on charts by accepting audio inquiries from individuals with visual impairments and do more complex reasoning using Large Language Models.
GCT-VAE-GAN: An Image Enhancement Network for Low-Light Cattle Farm Scenes by Integrating Fusion Gate Transformation Mechanism and Variational Autoencoder GAN

In the context of cattle farm environments, intricate environmental interferences have presented challenges that impede seamless data acquisition. This paper introduces a novel approach, the integration of a fusion gate transformation mechanism and a variational autoencoder GAN, which we term GCT-VAE-GAN, aimed at enhancing low-light images from cattle farm settings. Initially, our approach involves the design of an encoding network tasked with augmenting the original images. Subsequently, we advance our methodology by formulating a generative network to effectively address the challenges of image diversification and poor image quality. Notably, the inclusion of an attention mechanism block within the FFN layer facilitates the fusion of these extracted features, resulting in the generation of high-quality images. Furthermore, to achieve proficient image discrimination, we implement a dual-discriminator structured discriminative network for the conclusive image discrimination task. The culmination of our approach involves the formulation of a comprehensive joint loss function, thereby constituting the core of the model’s loss module. Moreover, through comparative experiments, we aptly demonstrate the remarkable superiority of the GCT-VAE-GAN approach. The conducted experiments reveal the model’s consistent performance and resilience under varying illumination scenarios. The outcomes of our study underscore its significant relevance in elevating the quality of low-light images within cattle farm contexts. Furthermore, our approach exhibits the potential to enhance the efficacy of computer vision tasks, signifying a notable stride toward improved agricultural imaging techniques.
Joint Preloading and Bitrate Adaptation for Short Video Streaming

Short videos have become one of the most popular content mobile users consume nowadays. However, unlike traditional videos, users watch many short videos each time and frequently skip those not of their interest. Not taking into account this factor, conventional video streaming methods result in a large amount of data wastage. Moreover, short videos are usually streamed over time-varying network conditions, which requires preloading to provide satisfactory Quality of Experience (QoE). In this paper, we formulate the QoE-Wastage trade-off optimization problem in short video streaming as a multi-objective optimization problem. We then develop a lightweight joint preloading and bitrate adaptation algorithm that utilizes cross-user behaviors to decide a suitable amount of preloaded data for each video. In addition, our method dynamically adjusts video bitrate per chunk basis to achieve high user QoE. Experimental results show that the proposed method improves QoE by approximately 12% to 45% and reduces data wastage by up to 58% compared to existing methods.
Gaze-Based Shared Autonomy Framework With Real-Time Action Primitive Recognition for Robot Manipulators

Robots capable of robust, real-time recognition of human intent during manipulation tasks could be used to enhance human-robot collaboration for activities of daily living. Eye gaze-based control interfaces offer a non-invasive way to infer intent and reduce the cognitive burden on operators of complex robots. Eye gaze is traditionally used for "gaze triggering" (GT) in which staring at an object, or sequence of objects, triggers pre-programmed robotic movements. We propose an alternative approach: a neural network-based "action prediction" (AP) mode that extracts gaze-related features to recognize, and often predict, an operator's intended action primitives. We integrated the AP mode into a shared autonomy framework capable of 3D gaze reconstruction, real-time intent inference, object localization, obstacle avoidance, and dynamic trajectory planning. Using this framework, we conducted a user study to directly compare the performance of the GT and AP modes using traditional subjective performance metrics, such as Likert scales, as well as novel objective performance metrics, such as the delay of recognition. Statistical analyses suggested that the AP mode resulted in more seamless robotic movement than the state-of-the-art GT mode, and that participants generally preferred the AP mode.
Problems of readiness of the secondary vocational education system for the growth of the incoming population

The relevance of the study of this problem is due to the growing burden on the secondary vocational education system, namely the increase in the number of graduates of 9th and 11th grades who choose this level of education after school. The purpose of the article was to study the problems, consequences and risks of the growth of the number of entrants to the secondary vocational education system.
Sampling Equivariant Self-Attention Networks for Object Detection in Aerial Images

Objects in aerial images show greater variations in scale and orientation than in other images, making them harder to detect using vanilla deep convolutional neural networks. Networks with sampling equivariance can adapt sampling from input feature maps to object transformation, allowing a convolutional kernel to extract effective object features under different transformations. However, methods such as deformable convolutional networks can only provide sampling equivariance under certain circumstances, as they sample by location. We propose sampling equivariant self-attention networks, which treat self-attention restricted to a local image patch as convolution sampling by masks instead of locations, and a transformation embedding module to improve the equivariant sampling further. We further propose a novel randomized normalization module to enhance network generalization and a quantitative evaluation metric to fairly evaluate the ability of sampling equivariance of different models. Experiments show that our model provides significantly better sampling equivariance than existing methods without additional supervision and can thus extract more effective image features. Our model achieves state-of-the-art results on the DOTA-v1.0, DOTA-v1.5, and HRSC2016 datasets without additional computations or parameters.
Message Patterns

The sixth block of patterns we cover is Message Patterns or patterns at the level of an individual message. Their pattern language names are: Significant Event, Defamiliarizing Effect, Anchor Messages, Diegetic Messages, Message Change, Message Invariant, UI Message, Original Message, Opposition Messages, Implementation Discourse, Linked Messages, Gossip, Abnormal Value, Message Context, Marked Messages, Incomplete History, Message Interleave, and Fiber Bundle.
Entrepreneurship Teaching Exercises: integrating generative AI

The use of Artificial Intelligence (AI) software has recently increased exponentially. Generative AI capabilities have moved from fiction to fact. This technology is changing the way we engage in Entrepreneurship, research it, and teach it. The significant impact on Entrepreneurship teaching is the focus of this paper. Any instructor, regardless of tech background, can and should be integrating AI into their courses right now. We describe and discuss our experience with this process and our deliberative approach to create value for students. We also provide several templates of Entrepreneurship exercises that will appeal to a variety of students and can be integrated in virtually any Entrepreneurship course.
Cooperative Control of UGV-UAV with Focus on Teaching-Learning Processes

This article presents an application based on the learning of cooperative control of robots, for its development is used a 3D environment made in the UNITY 3D software; and a mathematical simulation software MATLAB where advanced control algorithms are developed, this with the objective that the unmanned ground vehicle (UGV) and the unmanned aerial vehicle (UAV) execute an autonomous locomotion and can perform cooperative tasks, analyzing the stability of the proposed control based on the behavior of control errors and showing the effectiveness of the controller, as in previous works are shown training controllers between robot of the same type, in this case the training control is carried out between heterogeneous robots. Finally, the experimental tests are presented with a user iteration and immersion in the virtual environment where the results obtained from the proposed cooperative control are validated, in order to execute the best design of the cooperative controller in the physical omnidirectional terrestrial and unmanned aerial mobile robots obtaining a significant cost reduction in the development of the experimental tests.
The Design and Development of Gamified Mixed Reality for Education with a Focus on the Implementation of Dental Morphology Application

The concept of using mixed reality (MR) as a teaching tool in medical education is undergoing rapid development, and gamification has become a core design tool in many educational experiences. This paper aims to demonstrate how to design and develop a gamified MR application for dentistry education in a dental morphology course lab using a Microsoft HoloLens 2 device, which allows students to experience MR using a head-mounted device (HMD) without interfering with their physical reality thus stimulating a realistic learning experience using virtual objects. In this paper, we design a gamified MR application that utilizes the enhanced ARCS model for gamification of learning, called the ARCS+G model, and apply the rapid application development model (RAD) to develop the MR application, which was built by combining imported 3D models and the mixed reality toolkit (MRTK) in the Unity game engine. Students are encouraged to actively explore different dental structures, define tooth names, and learn the FDI World Dental Federation notation by manipulating and interacting with the 3D tooth model. The paper aims to demonstrate that this MR application can positively aid in the development of a comprehensive understanding of dental morphology.
Terminology and Motives for Central Bank Digital Currency

This chapter introduces the notion of central bank digital currency (CBDC) and the motives for its establishment. It presents an overview to the current situation of digital currencies worldwide. It also presents the main legal, technical and economic questions related to CBDC and the structure the work will follow in order to answer these questions. The existing forms of electronic and digital money as well as the most commonly named motives will be revealed and discussed in more detail.
Impact of Network Security Enhancement on Work from Home

With the advancement of technologies new trends are introducing in the market to accomplish the industrial task in efficient and convenient manner. The work from home culture is one of the significant cultures introduced in the pandemic situation and it was too successful model during the time and also in the present time. The crucial section of the work from is support of network and communication technology with security considerations. The network security is one of the important aspect associated with, to overcome this situation there are multiple options are taking in the account similarly one of the idea about to train the employee with advance network training remain exited. The idea is typical to implement as this is concern from the technology learning, still lot of positive opinions are there to support this action. So it is projected that the advance network training is significant action to prevent cyber and security attack for the duration of work from home. The present research paper is a comprehensive study with the opinion of respondents with statistical data analysis.
Survey of Explainable AI Techniques: A Case Study of Healthcare

With its unparalleled predictive capabilities and decision-making power, artificial intelligence (AI) has transformed countless disciplines. Nevertheless, as AI technology advances in complexity and ubiquity, so does the requirement for transparency and interpretability increase commensurately. The opaqueness of AI models makes them difficult to comprehend, which leads to mounting concerns regarding their application. To address this issue effectively, Explainable Artificial Intelligence (XAI) has emerged as an attempt to demystify how machine learning model decisions are made possible by offering comprehensible explanations for opacities caused by variations in input features or noise in data collection processes using methods such as LIME or SHAP especially vital in domains such as health care where ethical considerations outweigh those faced in other areas. This study provides an overview of XAI techniques, a case study that talks about the importance of using techniques such as LIME and SHAP in a healthcare organization to predict whether or not the patient is likely to get a heart disease or not, why and how the outcomes have been obtained. In the concluding section, the paper elaborates on the challenges facing XAI and outlines prospective future directions for research in this domain.
EthFor: Forensic Investigation Framework for Ethereum Blockchain

The use of blockchain technology has become increasingly popular for protecting data storage and transmission through decentralised, peer-to-peer networks. This technology is essentially a synchronised decentralised database managed by Distributed Ledger Technology (DLT), which shifts trust from a centralised network to a completely decentralised network of nodes. As a result, information is securely preserved and transmitted across numerous nodes spread across wide areas, ensuring that transactions remain private, secure, and immutable. Blockchain offers various benefits, including traceability, accountability, transparency, and provenance of recorded transactions on the ledger. Therefore, this study proposes a forensic investigation framework for Ethereum blockchain.
Online Meeting Summarization Based on Text and Image Processing

The rise of online meetings has led to an increase in the amount of audio and visual data that needs to be processed in order to extract relevant information. To address this issue, researchers have proposed a novel approach to online meeting summarization based on text and image processing. This approach involves using natural language processing techniques to extract important information from the audio transcript of the meeting and image processing techniques to extract key visual information from the video stream. The proposed system takes into account various factors, such as speaker turns, sentiment analysis, and key visual elements, and uses these factors to generate a comprehensive summary of the meeting. The system has been evaluated on a dataset of online meetings, and the results show that it is able to generate accurate and concise summaries. This approach has significant potential for improving the efficiency of online meetings by enabling participants to quickly review the most important points discussed during the meeting. Furthermore, it could also be used for automated transcription and captioning, which could help make online meetings more accessible to individuals with hearing impairments. Overall, online meeting summarization based on text and image processing has the potential to greatly enhance the effectiveness and accessibility of online communication. A method to extract keywords from educational video transcripts from Massive Open Online Courses (MOOC) is discussed. The approach is primarily based totally on Regular Expression Grammar Rule technique to identify the Noun Chunks in the textual content of the transcript. Extracting keywords helps in finding out the specifically important part of the educational material.
Conversion of Natural Language to SQL Query

This paper outlines a method for efficiently automating the conversion of Natural Language Query to Structured Query Language (SQL) queries. SQL is a standard tool for relational database management systems. The correct SQL query with proper keywords and syntax must be entered to obtain or manipulate data from such databases. Therefore, we put forward Natural Language Sequential Query Language “NLSQL”, a system in natural language processing to get around this problem and convert natural language queries to SQL queries. To revamp prevailing advancements in this field, we propose three times state-of-the-art parsing and implementation for JOIN queries with our system’s support for multiple tables in a rule-based system. Along with basic queries, complex commands can also be handled by this system. After experimenting and testing this work on a number of natural language queries we were able to achieve convincing results.
A Byte Transformation Technique for Lossy Image Compression

With the advancement of IT field and social media communications, the transaction of images is increasing day by day. Hence, it is required to do the image compression for storage as well as transmission. In this paper, we discuss a transformation-based technique for grayscale image compression. In this approach, a block of four pixel data is transformed in to three bytes of data. Later, additional bits are added by suitable encoding method to achieve high-quality reconstructed images. Though it is lossy, the accuracy achieved at same PSNR levels compared to JPEG is higher. Compression ratio achieved is also comparable.