Reviewerly/exHarmony huggingface dataset:
Blockchain of Things (BCoT): The Fusion of Blockchain and IoT Technologies

Blockchain, as well as Internet of Things (IoT), is considered as two major disruptive emerging technologies. However, both of them suffer from innate technological limitations to some extent. IoT requires strengthening its security features while Blockchain inherently possesses them due to its extensive use of cryptographic mechanisms and Blockchain—in an inverted manner—needs contributions from the distributed nodes for its P2P (Peer-to-Peer) consensus model while IoT rudimentarily embodies them within its architecture. This chapter, therefore, acutely dissects the viability, along with prospective challenges, of incorporating Blockchain with IoT technologies—inducing the notion of Blockchain of Things (BCoT)—as well as the benefits such consolidation can offer.
Information Extraction using Tokenization And Clustering Methods

World Wide Web has become a powerful platform for retrieval and storage of information. It is a collection of text, image and multimedia files in structured, semi structured and unstructured form. These tremendous volumes of information cannot be processed so simply. An efficient and useful algorithm is required to discover information from these data. Text mining is a method for extracting meaningful information from large volume of data. Unstructured text is easily processed by humans but it is harder for machines. Text mining task involve methods such as tokenization, feature extraction and clustering.
A robust verification system for recruitment process by using blockchain technology

In this competitive world, it is hard to get a job. It requires some specific qualifications and experience according to the post. If a person is not fulfilling these required entities, then not able to apply for that post. Therefore, some people, who are not eligible may use different forgery approaches like fake mark sheets, fake experience certificates, fake medical certificates, etc. However, despite being these fake certificates, some people may have a criminal background also. Therefore, large numbers of resources are required to verify the educational records, criminal background, and experience of a person. Therefore, the proposed system provides an efficient solution to these problems using blockchain technology. In the proposed work, we used three different modules like college, organisation, and police. The college is responsible for providing academic qualifications and the organisation is responsible for providing experience while police are responsible for proving and verification of the criminal record. The proposed system is implemented on the Ethereum blockchain platform and effectively can be used by the organisations for verifying the record of their employees.
Survey on clothing image retrieval with cross-domain

Abstract The paper summarizes the research progress on critical region recognition and deep metric learning to achieve accurate clothing image retrieval in cross-domain situations. Critical region recognition is of great value for the clothing feature extraction, effectively improving retrieval accuracy. The accuracy will decrease when solving difficult samples with similar features but different categories. Nowadays, deep metric learning is an effective way to solve this problem, which utilizes the optimization of different loss functions and ensemble network to strengthen the discrimination of clothing features. Therefore, through comparison of the experimental results of different algorithms and analysis of the accuracy of cross-domain clothing retrieval, it is demonstrated that the improvement of the retrieval accuracy in the future mainly depends on clothing important feature extraction and clothing feature discrimination.
RNN Neural Network Model for Chinese-Korean Translation Learning

Translation is the expression of words into other forms so that they are mutually understandable. However, the current Chinese-Korean translation information conversion model is not mature enough, and there are many defects and errors. This paper proposes an improved RNN neural network translation model. The translation model proposed in this study is an upgraded RNN neural network (a form of recurrent neural network). A decoder matching mode is included in the model, allowing it to learn both alignment and transformation at the same time. This allows for quicker neural network training, resulting in increased translation speed and efficiency. In addition, this article compares the BLEU scores of the BilingualCorpus Chinese-Korean corpus I, II, and III with those of the standard translation model. The test results suggest that the enhanced RNN model described in this research has an average BLEU score of roughly 45 points. The conventional model’s average BLEU score is just approximately 30 points. The BLEU score of the model in this paper has increased by about 15 points, indicating that the translation quality of the translation model in this paper has been significantly improved.
Data Handling Process in Extended Reality (XR) When Delivering Technical Instructions

Purpose: Extended reality (XR) is a promising new medium that creates environments combining real and virtual elements or offers a completely virtual environment for people to experience. In the field of technical communication, XR offers a plethora of possibilities, such as augmenting critical instructions in a work environment. On the downside, XR brings about challenges. For example, issues of privacy and security require more attention due to the risks involved with XR devices continuously collecting data from the users and their surroundings. More knowledge concerning the use of XR as a medium to deliver technical instructions is required. In this article, I address this need by explaining how XR handles data.&lt;br/&gt; Methods: To find out how XR handles data, I used relevant previous research (33 papers and four books) as data for thematic analysis. I coded data systematically according to how XR has been used before and the phases that can be seen in the process of data handling in XR when it is used as a medium for technical instructions.&lt;br/&gt; Results: The data handled in XR can be divided into instructional data, such as assembly instructions, and collected data that XR equipment collects while someone is using it. Data handling in XR can be seen as a process. Based on the thematic analysis, I found six different phases of data handling: collection, processing, storage, transfer, combining, and presentation.&lt;br/&gt; Conclusion: The phases of data handling in XR illustrate in general what happens to the data in XR and what kind of data the equipment collects. My findings add to our understanding of XR as a medium to deliver technical instructions. They also offer a usable framework for mapping the differences between XR and other media as a way to deliver technical instructions.
Privacy Prevention of Big Data Applications: A Systematic Literature Review

This paper focuses on privacy and security concerns in Big Data. This paper also covers the encryption techniques by taking existing methods such as differential privacy, k-anonymity, T-closeness, and L-diversity. Several privacy-preserving techniques have been created to safeguard privacy at various phases of a large data life cycle. The purpose of this work is to offer a comprehensive analysis of the privacy preservation techniques in Big Data, as well as to explain the problems for existing systems. The advanced repository search option was utilized for the search of the following keywords in the search: “Cyber security” OR “Cybercrime”) AND ((“privacy prevention”) OR (“Big Data applications”)). During Internet research, many search engines and digital libraries were utilized to obtain information. The obtained findings were carefully gathered out of which 103 papers from 2,099 were found to gain the best information sources to address the provided study subjects. Hence a systemic review of 32 papers from 103 found in major databases (IEEExplore, SAGE, Science Direct, Springer, and MDPIs) were carried out, showing that the majority of them focus on the privacy prediction of Big Data applications with a contents-based approach and the hybrid, which address the major security challenge and violation of Big Data. We end with a few recommendations for improving the efficiency of Big Data projects and provide secure possible techniques and proposed solutions and model that minimizes privacy violations, showing four different types of data protection violations and the involvement of different entities in reducing their impacts.
SEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models

Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing.The canonical utterance is often lengthy and complex due to the compositional structure of formal languages.Learning to generate such canonical utterance requires significant amount of data to reach high performance.Fine-tuning with only few-shot samples, the LMs can easily forget pretrained knowledge, overfit spurious biases, and suffer from compositionally out-of-distribution generalization errors.To tackle these issues, we propose a novel few-shot semantic parsing method -SEQZERO.SEQZERO decomposes the problem into a sequence of sub-problems, which correspond to the sub-clauses of the formal language.Based on the decomposition, the LMs only need to generate short answers using prompts for predicting sub-clauses.Thus, SEQZERO avoids generating a long canonical utterance at once.Moreover, SEQZERO employs not only a fewshot model but also a zero-shot model to alleviate the overfitting.In particular, SEQZERO brings out the merits from both models via ensemble equipped with our proposed constrained rescaling.SEQZERO achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split. 1
Two-stage deep learning framework for sRGB image white balance

Abstract This work aims to correct white-balance errors in sRGB images. These white-balance errors are hard to fix due to the nonlinear color-processing procedures applied by camera image signal processors (ISP) to produce the final sRGB colors. Camera ISPs apply these nonlinear procedures after the essential white-balance step to render sensor raw images to the sRGB space through a camera-specific set of tone curves and look-up tables. To correct improperly white-balanced images, projecting non-linear sRGB colors back to their original raw space is required. Recent work formulates the problem as an image translation problem, where input sRGB colors are mapped using nonlinear polynomial correction functions to fix such white-balance errors. In this work, we show that correcting white-balance errors in sRGB images through a global color mapping followed by spatially local adjustments, learned in an end-to-end training, introduces perceptual improvements in the final results. Qualitative and quantitative comparisons with recently published methods for camera-rendered image white balancing validate our method’s efficacy and show that our method achieves competitive results with state-of-the-art methods.
Performance and Usability Evaluation of an Extended Reality Platform to Monitor Patient’s Health during Surgical Procedures

An extended-reality (XR) platform for real-time monitoring of patients' health during surgical procedures is proposed. The proposed system provides real-time access to a comprehensive set of patients' information, which are made promptly available to the surgical team in the operating room (OR). In particular, the XR platform supports the medical staff by automatically acquiring the patient's vitals from the operating room instrumentation and displaying them in real-time directly on an XR headset. Furthermore, information regarding the patient clinical record is also shown upon request. Finally, the XR-based monitoring platform also allows displaying in XR the video stream coming directly from the endoscope. The innovative aspect of the proposed XR-based monitoring platform lies in the comprehensiveness of the available information, in its modularity and flexibility (in terms of adaption to different sources of data), ease of use, and most importantly, in a reliable communication, which are critical requirements for the healthcare field. To validate the proposed system, experimental tests were conducted using instrumentation typically available in the operating room (i.e., a respiratory ventilator, a patient monitor for intensive care, and an endoscope). The overall results showed (i) an accuracy of the data communication greater than 99 %, along with (ii) an average time response below ms, and (iii) satisfying feedback from the SUS questionnaires filled out by the physicians after intensive use.
Improving Micro-video Recommendation via Contrastive Multiple Interests

With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention. However, existing micro-video recommendation models rely on expensive multi-modal information and learn an overall interest embedding that cannot reflect the user's multiple interests in micro-videos. Recently, contrastive learning provides a new opportunity for refining the existing recommendation techniques. Therefore, in this paper, we propose to extract contrastive multi-interests and devise a micro-video recommendation model CMI. Specifically, CMI learns multiple interest embeddings for each user from his/her historical interaction sequence, in which the implicit orthogonal micro-video categories are used to decouple multiple user interests. Moreover, it establishes the contrastive multi-interest loss to improve the robustness of interest embeddings and the performance of recommendations. The results of experiments on two micro-video datasets demonstrate that CMI achieves state-of-the-art performance over existing baselines.
DEVELOPMENT OF MOBILE VIRTUAL FIELD TRIPS IN IJEN CRATER GEOSITES BASED ON 3600 AUTO STEREOSCOPIC AND GEOSPATIAL TECHNOLOGY AS GEOGRAPHY LEARNING MEDIA

The Covid-19 pandemic limits the space for teachers and students in the Geography learning process, thus affecting the achievement of the competencies and capabilities of geography students. The purpose of this research is to develop Ijen Geosites Mobile virtual field trip (M-VFTs) media to help facilitate Geography learning. This study aims to develop Ijen Crater Geosites based on Mobile Virtual Field Trips media based on 3600 Auto Stereoscopic and Geospatial Technology. This research is included in research and development by adopting the PLOMP model. The results obtained consist of making M-VFTs related to the Ijen Geosites study with interactive informative access that can be easily accessed by users. The results of this study can relate the risk of limitations in obtaining information during actual visits, especially in learning geography, so that it can help students learn to be active, independent and meaningful through observation and exploration activities.
Application of Artificial Intelligence Combined with 5G Technology in the Reform of English Teaching in Universities

The language assistance and learning sectors have currently undergone restructuring in the period of fifth-generation (5G) communication and artificial intelligence, influenced by technologies, cloud services, learning techniques, speaker identification, language processing, virtual environments, expanded actuality, and blended actuality. This study proposes a routing protocol of modernized energy-optimized low-energy adaptive clustering hierarchical protocol (M-LEACH) using artificial intelligence and 5G Internet technologies for online English teaching. A dataset of 6,600,000 items, containing 3,250,000 favorable and unfavorable texts in English, is employed. The dataset is preprocessed using normalization to eliminate the impulse noises. Discriminant features are extracted using a variational autoencoder (VAE), and a random forest (RF) classifier is used to classify the features with accurate performance. The performance of the protocol is measured in terms of transmission rate, alive nodes, energy consumption, and a number of transmitted packets. Results show that the proposed M-Leach protocol provides a high transmission rate, maximum transmitted packets, more alive nodes, and minimum energy consumption as compared to other protocols. The proposed protocol will transform online English teaching from closed to open, and passive to active learning, dramatically changing the time and space scenarios, as well as the supply levels of English education.
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting Much work on biases in natural language processing has addressed biases linked to the social and cultural experience of English speaking individuals in the United States.We seek to widen the scope of bias studies by creating material to measure social bias in language models (LMs) against specific demographic groups in France.We build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages while also characterizing biases that are specific to each country and language.We introduce 1,677 sentence pairs in French that cover stereotypes in ten types of bias like gender and age.1,467 sentence pairs are translated from CrowS-pairs and 210 are newly crowdsourced and translated back into English.The sentence pairs contrast stereotypes concerning underadvantaged groups with the same sentence concerning advantaged groups.We find that four widely used language models (three French, one multilingual) favor sentences that express stereotypes in most bias categories.We report on the translation process, which led to a characterization of stereotypes in CrowS-pairs including the identification of US-centric cultural traits.We offer guidelines to further extend the dataset to other languages and cultural environments.
Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images

Gaze is an excellent indicator and has utility in that it can express interest or intention and the condition of an object. Recent deep-learning methods are mainly appearance-based methods that estimate gaze based on a simple regression from entire face and eye images. However, sometimes, this method does not give satisfactory results for gaze estimations in low-resolution and noisy images obtained in unconstrained real-world settings (e.g., places with severe lighting changes). In this study, we propose a method that estimates gaze by detecting eye region landmarks through a single eye image; and this approach is shown to be competitive with recent appearance-based methods. Our approach acquires rich information by extracting more landmarks and including iris and eye edges, similar to the existing feature-based methods. To acquire strong features even at low resolutions, we used the HRNet backbone network to learn representations of images at various resolutions. Furthermore, we used the self-attention module CBAM to obtain a refined feature map with better spatial information, which enhanced the robustness to noisy inputs, thereby yielding a performance of a 3.18% landmark localization error, a 4% improvement over the existing error and A large number of landmarks were acquired and used as inputs for a lightweight neural network to estimate the gaze. We conducted a within-datasets evaluation on the MPIIGaze, which was obtained in a natural environment and achieved a state-of-the-art performance of 4.32 degrees, a 6% improvement over the existing performance.
Visual object recognition ability is not related to experience with visual arts

Visual arts require the ability to process, categorize, recognize, and understand a variety of visual inputs. These challenges may engage and even influence mechanisms that are also relevant for visual object recognition beyond visual arts. A domain-general object recognition ability that applies broadly across a range of visual tasks was recently discovered. Here, we ask whether experience with visual arts is correlated with this domain-general ability. We developed a new survey to measure general visual arts experience and use it to measure arts experience in 142 individuals in whom we also estimated domain-general object recognition ability. Despite our measures demonstrating high reliability in a large sample size, we found substantial evidence (BF01 = 9.52) for no correlation between visual arts experience and general object recognition ability. This suggests that experience in visual arts has little influence on object recognition skills or vice versa, at least in our sample ranging from low to moderately high levels of arts experience. Our methods can be extended to other populations and our results should be replicated, as they suggest some limitations for the generalization of programs targeting visual literacy beyond the visual arts.
How racial animus forms and spreads: Evidence from the coronavirus pandemic

This paper studies the formation and the spread of crisis-driven racial animus during the coronavirus pandemic. Exploiting plausibly exogenous variation in the timing of the first COVID-19 diagnosis across US areas, we find that the first local case leads to an immediate increase in local anti-Asian animus, as measured by Google searches and Twitter posts that include a commonly used derogatory racial epithet. This rise in animus specifically targets Asians and mainly comes from users who use the epithet for the first time. These first-time ch-word users are more likely to have expressed animosity against non-Asian minorities in the past, and their interaction with other anti-Asian individuals predicts the timing of their first ch-word tweets. Moreover, online animosity and offline hate incidents against Asians both increase with the salience of the connection between China and COVID-19; while the increase in racial animus is not associated with the local economic impact of the pandemic. Finally, the pandemic-driven racial animus we documented may persist beyond the duration of the pandemic, as most racist tweets do not explicitly mention the virus.
A Novel Image Encryption Algorithm Based on Voice Key and Chaotic Map

This paper proposes a new image encryption algorithm. First, time-domain and frequency-domain features of the user’s voice are extracted to generate a voice key. Second, the key is iterated through a chaotic map multiple times to map the key data to the chaotic oscillation region, and, subsequently, the parameters of the oscillation area are used to encrypt the user’s image. Third, at the time of decryption, the user’s latest voice data are re-extracted to generate a new voice key and decrypt the encrypted image. The encrypted image cannot be successfully decrypted if there are differences between the two extracted voices in the time or frequency domain. Finally, the experiments are performed using 80 groups of face images and voice data, all of which pass the encryption and decryption experiments. In addition, various safety tests have been carried out on the algorithm. The key sensitivity of the algorithm is verified by the normalized cross-correlation parameter Cncc. The effective anti-attack ability of the algorithm is verified by measuring the correlation between adjacent pixels, the number of changing pixel rate (NPCR) and the unified averaged changed intensity (UACI). The key space of the proposed algorithm is greater than 2100, and it has good anti-cracking ability.