Reviewerly/exHarmony huggingface dataset:
Advanced System for Enhancing Location Identification through Human Pose and Object Detection

Location identification is a fundamental aspect of advanced mobile robot navigation systems, as it enables establishing meaningful connections between objects, spaces, and actions. Understanding human actions and accurately recognizing their corresponding poses play pivotal roles in this context. In this paper, we present an observation-based approach that seamlessly integrates object detection algorithms, human pose detection, and machine learning techniques to effectively learn and recognize human actions in household settings. Our method entails training machine learning models to identify the common actions, utilizing a dataset derived from the interaction between human pose and object detection. To validate our approach, we assess its effectiveness using a diverse dataset encompassing typical household actions. The results demonstrate a significant improvement over existing techniques, with our method achieving an accuracy of over 95% in classifying eight different actions within household environments.. Furthermore, we ascertain the robustness of our approach through rigorous testing in real-world environments, demonstrating its ability to perform well despite the various challenges of data collection in such settings. The implications of our method for robotic applications are significant, as a comprehensive understanding of human actions is essential for tasks such as semantic navigation. Moreover, our findings unveil promising opportunities for future research, as our approach can be extended to learn and recognize a wide range of other human actions. This perspective, which highlights the potential leverage of these techniques, provides an encouraging path for future investigations in this field.
Exploration of the Mountain Sacrificial System in the Han Dynasty

Natural worship is a unique spiritual activity of human civilization that has existed since ancient times, arising from the awe-inspiring feeling that humans have towards mountains, rivers, and all things in nature. The mountain sacrifice system is a ritual practice that developed from the spiritual development of natural worship. Since the mythological era, the worship of natural phenomena and objects has existed, and Confucianism incorporated the ethics of nature into its doctrine, including mountains and rivers in its philosophical concept of Earth. During the Han Dynasty, rulers reorganized and reformed the mountain sacrifice system and formed the core ritual of the Taishan Mountain sacrifice. The various functions of the Han Dynasty mountain sacrifice ceremony reveal social customs during the Western Han period and Confucian related rituals. The changes in the mountain sacrifice policy during the Han Dynasty reflect the political strategies of rulers and the connection between Confucianism and its rituals. This article analyzes the changes in the Han Dynasty mountain sacrifice system and the basic situation of Han people's worship and beliefs through close reading of texts. It aims to demonstrate the peculiarities of the Han Dynasty mountain sacrifice system and its connection with Confucianism and its rituals.
A binocular vision method for precise hole recognition in satellite assembly systems

The prevalent manual assembly process for satellites is marked by inefficiencies and a heightened risk of collision, thus limiting its scalability and potential for integration. To address these limitations, the adoption of industrial robots for an intelligent assembly mode has emerged as a promising alternative. Nonetheless, the implementation of an industrial robot satellite assembly system brings its own challenges, including low accuracy of the robot, large manufacturing errors of the satellite products and accumulated errors in the picking and placing process. As the main positioning feature, the detection of holes plays an important role in solving these problems. This paper introduces a novel binocular vision hole recognition system, coupled with an associated hole recognition algorithm, aiming at enhancing the detection accuracy of characteristic holes of satellite products. The proposed methodology includes the development of a confidence-based indicator to assess the quality of two-dimensional hole detection, and an iterative optimization of the fitting ellipse. Furthermore, a confidence-based neighborhood denoising two-stage fitting method is designed for accurate hole positioning. Experimental results validate the proposed methodology, showcasing a recognition accuracy of 0.060 mm and an assembly accuracy within 0.2 mm, thereby fulfilling the requirements of the industrial robot satellite assembly system.
Pelatihan Mendesain Animasi 3D Kupu-Kupu Untuk Meningkatkan Daya Tarik Calon Mahasiswa Pada Prodi Teknologi Komputer

Vocational high school students who have graduated from vocatonal hight school always have difficulty deciding between going straight to work or continuing their studies. Major selection is included in the educational process, which has the scope of determining future career success. The study program chosen must be in accordance with the interests and abilities possessed by students. This community service trains vocational or high school students to gain skills in designing a 3D butterfly animation. This community service aims to promote the Computer Technology Study Program to SMK or SMA students so that they are interested in registering at the Medan LP3I Polytechnic. The method used is the lecture and training method. The Powepoint 2019 application is used in the material, while the 3D Max Application is used to design butterfly animations
Multimodal Knowledge Triple Extraction Based on Representation Learning

Knowledge based visual question-answering is an emerging technique that combines computer vision and natural language processing to address image-based questions. This approach requires the model to possess internal reasoning ability and incorporate external knowledge to enhance its generalization performance. Knowledge graphs are commonly employed to integrate various types of knowledge, such as image knowledge, text knowledge, and basic common sense, into visual language question answering models, significantly enhancing their interpretability. However, introducing knowledge into these models often involves retrieval and pre-training methods, which can introduce noise due to the local correlation among knowledge sources and the existence of modality gaps, thereby affecting the model's performance. To mitigate this issue, this paper proposes a multimodal knowledge extraction approach based on distributed representation learning. The approach models inexpressible multimodal facts using explicit triples, considering the semantic gap between visual and textual modalities. Cross-modal representations are obtained through an attention mechanism, resulting in knowledge triplets comprising visual features, cross-modal representations, and text features. By incorporating these knowledge triplets into the visual language question answering model, the task is transformed into pattern matching using knowledge triples. The proposed approach comprehensively considers multiple factors, is not restricted to specific forms of knowledge, and can effectively incorporate a substantial amount of knowledge. Experimental results demonstrate its superiority, with a performance improvement of 0.5% on the OKVQA dataset compared to the baseline model, highlighting its strong generalization ability.
Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning

Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds.
A Hybrid Model for Multilingual OCR

Large-scale document processing pipelines are required to recognize text in many different languages. The writing systems for these languages cover a diverse set of scripts, such as the standard Latin characters, the logograms of Chinese, and the cursive right-to-left of Arabic. Multilingual OCR continues to be a challenging task for document processing due to the large vocabulary sizes and diversity of scripts. This work introduces a multilingual model that recognizes over nine thousand unique characters and seamlessly switches between ten different scripts. Our transformer-based encoder-decoder approach combines a CTC objective on the encoder with a cross-entropy objective on the full autoregressive decoder. The hybrid approach allows the fast non-autoregressive encoder to be used in standalone mode or with the full autoregressive decoder. We evaluate our approach on a large multilingual dataset, where we achieve state-of-the-art character error rate results in all thirteen languages. We also extend the encoder with auxiliary heads to identify language, predict font, and detect vertical lines.
TextREC: A Dataset for Referring Expression Comprehension with Reading Comprehension

Referring expression comprehension (REC) aims at locating a specific object within a scene given a natural language expression. Although referring expression comprehension has achieved tremendous progress, most of today’s REC models ignore the scene texts in images. Scene text is ubiquitous in our society, and frequently critical to understand the visual scene. To study how to comprehend scene text in the referring expression comprehension task, we collect a novel dataset, termed TextREC, in which most of the referring expressions are related to scene text. Our TextREC dataset challenges a model to recognize scene text, relate it to the referring expressions, and select the most relevant visual object. We also propose a text-guided adaptive modular network (TAMN) to comprehend scene text associated with objects in images. Experimental results reveal that current state-of-the-art REC methods fall short on the TextREC dataset, while our TAMN gets inspiring results by integrating scene text.
ICDAR 2023 Competition on Video Text Reading for Dense and Small Text

Recently, video text detection, tracking and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignore extreme video texts challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, named DSText, which focuses on dense and small text reading challenge in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., ‘Game’, ‘Sports’, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task2)). During the competition period (opened on 15th February, 2023 and closed on 20th March, 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise the video text research in the community.
SAN: Structure-Aware Network for Complex and Long-Tailed Chinese Text Recognition

In text recognition, complex glyphs and tail classes have always been factors affecting model performance. Specifically for Chinese text recognition, the lack of shape-awareness can lead to confusion among close complex characters. Since such characters are often tail classes that appear less frequently in the training-set, making it harder for the model to capture its shape information. Hence in this work, we propose a structure-aware network utilizing the hierarchical composition information to improve the recognition performance of complex characters. Implementation-wise, we first propose an auxiliary radical branch and integrate it into the base recognition network as a regularization term, which distills hierarchical composition information into the feature extractor. A Tree-Similarity-based weighting mechanism is then proposed to further utilize the depth information in the hierarchical representation. Experiments demonstrate that the proposed approach can significantly improve the performances of complex characters and tail characters, yielding a better overall performance. Code is available at https://github.com/Levi-ZJY/SAN .
Pedestrian Re-identification Combined with Triplet Metric Learning and Dual-Domain Filtering in Noisy Scene

In actual power grid surveillance system, it is difficult for many pedestrian re-identification methods to extract pedestrian features at night, rainy or foggy days because of the insufficient illumination or the bad weather. Focusing on the pedestrian re–identification in noisy situations, this paper proposes to use the dual-domain filter decomposition to construct triples, which are used to train metric learning model to learn the robust features. Our proposed method mainly consists of two parts. Firstly, we analyze the distribution characteristic of image noise in surveillance videos and propose an image denoising method based on dual-domain filtering. Secondly, based on the separation effect of dual-domain filtering on image noise, this paper draws on the clustering ability of the triple loss and proposes a new way to construct the training triplet. In the training stage, different from the traditional triplet matrix learning, the original images with the low-frequency component and the high-frequency component generated by the dual-domain filtering are used as the input triplet. So the network can further suppress the noise components and improve the robustness of the feature. The experiments on Market-1501 and CUHK03 data sets show that our Rank-1 only drops 1.9% and 7.8% in noisy scene, which means the proposed method can make the accuracy of noisy pedestrian re-identification performance be close to that of the original non-noise pedestrian re-identification performance.
Weakly Supervised Information Extraction from Inscrutable Handwritten Document Images

State-of-the-art information extraction methods are limited by OCR errors. They work well for printed text in form-like documents, but unstructured, handwritten documents still remain a challenge. Adapting existing models to domain-specific training data is quite expensive, because of two factors, 1) limited availability of the domain-specific documents (such as handwritten prescriptions, lab notes, etc.), and 2) annotations become even more challenging as one needs domain-specific knowledge to decode inscrutable handwritten document images. In this work, we focus on the complex problem of extracting medicine names from handwritten prescriptions using only weakly labeled data. The data consists of images along with the list of medicine names in it, but not their location in the image. We solve the problem by first identifying the regions of interest, i.e., medicine lines from just weak labels and then injecting a domain-specific medicine language model learned using only synthetically generated data. Compared to off-the-shelf state-of-the-art methods, our approach performs $$>2.5\times $$ better in medicine names extraction from prescriptions.
AuthBlock: Authentication Framework Using Ethereum Blockchain

When employing authentication mechanisms to store user credentials, a subtle point to note is that they are easily vulnerable to cyber attacks like sharing of user data without their consent, password stealing on a large scale, etc. By decentralizing ownership of credentials and providing a framework for confirming one’s record in an unalterable chain of data, i.e., Distributed Ledger Technology (DLT) in general and blockchain can provide a solution. Blockchain technology can help reduce the risk of attacks and user data leaks through backdoors by establishing a secure platform for online service providers to authenticate users without a single point of failure. Blockchain is being utilized increasingly for trusted, decentralized, secure registration, authentication, and valuation of digital assets (assets, real estate, etc.) and transactions, governing interactions, recording data, and managing identity among numerous parties. Smart contracts are used to do transactions on the blockchain. This work aims to analyze the shortcomings of traditional authentication systems and hence provide a blockchain-based authentication solution to address them. In this paper, we suggest AuthBlock, a robust, lightweight, and secure blockchain-based authentication system. It can be used by multiple parties as an authentication framework in parallel without any interference. The proposed approach leverages the Ethereum blockchain along with its provision of smart contracts. The proposed method is tested on the Ethereum localnet created using Go Ethereum (Geth) and evaluated to analyze user authentication, verification, and cost.
A Systematic Study of Services and Security Model in Cloud Computing

Cloud computing is a technical model with on-demand network access, delivering various types of services, providing resources such as memory storage, applications, networking, databases, servers, etc. All these resources always keep with us but with one condition: “the internet.” The major purpose of this chapter is to know the security and services requirements and their solutions in a cloud computing environment. This rigorous research extensively explores the complex relationship between service delivery and security. It also looks at the issues and potential fixes for providing services that are trustworthy, efficient, and safe. Because of the accelerated pace of technological advancement, the networked nature of systems, and the rising reliance on internet services, the potential of threats and vulnerabilities has increased significantly. After that, the authors focus on what types of services are given in the cloud where each type of service provides different options like efficiency, accessibility, savings, flexibility, opportunity, innovation, and many more.
Commensurable Analysis of Scientific Communications Published in Reviews in Aquaculture Applying Scientometric Analysis

This study presents a longitudinal and visualization mapping of scientific communications published in Reviews in Aquaculture during the period 2011-2020, applying scientometric approaches to depict the scientific contributions, collaboration trends, and research hotspots in the subject of aquaculture.Metadata of 412 articles published in was retrieved and downloaded from Reviews in Aquaculture Scopus database.The network graphs were visualized using ' ' and ' ' software.The chronological growth of scientific VOSviewer Gephi communications published in the journal, most productive authors, institutions, and countries vis-à-vis collaboration trends amongst them were scrutinized.The subject facets engulfed by the journal were identified based on co-occurrence of keywords.The findings would be useful for strengthening collaborative research as well as to pay required attention towards the slenderly explored sub-domains in aquaculture.
ENHANCING IMAGE TRANSFER SECURITY IN FTP AND SMTP PROTOCOLS

When a user uploads their private data to the cloud, the data remains in the cloud as long as the service is running and even until the data is destroyed.In the given chain of events, there is no guarantee that the files will remain private and/or permanently deleted.A third party who is not the intended recipient of the file can view the file if he/she has access to the file storage.With most image-sharing services offered today, the image is transmitted over a protected channel but is not encrypted.This allows access to the files by an unintended recipient.In this project, we focus exclusively on image encryption.As images become an important part of digital communication, it is necessary to provide security and privacy during communication.To ensure security and user privacy, encryption is very important to protect the user from unauthorized access.Encryption is important because it allows you to securely protect data that you don't want anyone else to have access to.Encryption is a security method that encrypts information so that only authorized users can read it.It uses an encryption algorithm to generate cipher data that can only be read if decrypted.
Local Self-attention-based Hybrid Multiple Instance Learning for Partial Spoof Speech Detection

The development of speech synthesis technology has increased the attention toward the threat of spoofed speech. Although various high-performance spoofing countermeasures have been proposed in recent years, a particular scenario is overlooked: partially spoofed audio, where spoofed utterances may contain both spoofed and bona fide segments. Currently, the research on partially spoofed speech detection is lacking. The existing methods either train with partially spoofed speech at utterance level, resulting in gradient conflicting at the segment level, or directly train with segment level data, which requires segment labels that are difficult to obtain in practice. In this study, to better detect partially spoofed speech when only utterance labels are available, we formulate partially spoofed speech detection into a multiple instance learning (MIL) problem. The typical MIL uses a pooling layer to fuse patch scores as a whole, and we propose a hybrid MIL (H-MIL) framework based on max and log-sum-exp pooling methods, which can learn better segment representations to improve partially spoofed speech detection performance. Theoretical and experimental verification shows that H-MIL can effectively relieve the gradient conflicting and gradient vanishing problems. In addition, we analyze the local correlations between segments and introduce a local self-attention mechanism to enhance segment features, which further promotes the detection performance. In our experiments, we provide not only detection results at the segment and utterance levels but also some detailed visualization analysis, including the effect of spoof ratio and cross-dataset detection. The experimental results demonstrate the effective detection performance of our method at both the utterance and segment levels, especially when dealing with low spoof ratio attacks. The results confirm that our approach can better deal with partially spoofed speech detection than previous methods.
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Inside Precision MedicineVol. 10, No. 4 MarketplaceView these Featured SolutionsPublished Online:19 Aug 2023https://doi.org/10.1089/ipm.10.04.15AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 10Issue 4Aug 2023 InformationCopyright © GEN PublishingTo cite this article:View these Featured Solutions.Inside Precision Medicine.Aug 2023.49-49.http://doi.org/10.1089/ipm.10.04.15Published in Volume: 10 Issue 4: August 19, 2023PDF download