Reviewerly/exHarmony huggingface dataset:
The Analysis of Digital Innovation in Cross-Cultural Communication and its Influences on Sociable and Unsociable People

Anyone who travels from one country to another one may be confused and perplexed or even will experience overwhelmed fluctuations, which is called cultural shock.For anyone who works or lives abroad, this cultural shock is an inevitable and a normal phenomenon. [1]Cultural shock is not a mental illness, but a natural, or an uncomfortable response to a new environment.With the rapid development of the times and the explosion of information, people's understanding of other countries and cultures has also risen to a new level.Correspondingly, with the advancement of science and technology, the way of people's communication has gradually shifted from traditional communication to the current form of digital communication.For example, in the past, people often communicated face-to-face, but now people mainly communicate by digital modes, such as We Chat and QQ.In addition, there are more and more channels for people to retrieve information.In the past, people used to retrieve information mostly by reading books and newspapers, but now the developed network enables people to search and get the information they want easily and quickly.And big data will also recommend relevant information for users according to personal preferences.With the development of digital networks and the popularization of digital communication, the number of netizens has also exploded.In recent years, two kinds of people became popular in the network society, one is those people who are sociable, and the other is those people who are afraid to communicate with people.This article will take the cross-cultural innovation--digital communication as the background to analyze how this phenomenon exerts influences on the virtual life of those who are good at socializing with others and those who are afraid of doing so in real life. [2]This article will analyze the following parts: who are those people good at socializing and who are the ones bad at it ; the reasons why the era of digital communication came; the "freedom of speech" brought by social applications in the era of digital communication; the speech platforms the digital communication provided; the job opportunities provided by the era of digital communication; the impact of the era of digital communication on social anxiety in real life; the impact of the era of digital communication on social anxiety in online life; and a summary of this research.
The Application of AI Technologies in English Education in Industry 4.0

This article will briefly present the great changes that took place in English education in the background of industry 4.0, focusing especially on how modern Artificial Intelligence (AI) technologies in industry 4.0 has influenced the way of English teaching and learning.First, the general changes in English education and the features of traditional way of English teaching will be concluded, followed by a brief introduction of the circle of English learning.Based on the circle, four technologies (intelligent tutoring system, chatbot, virtual reality, and automatic evaluation system) will be introduced to illustrate how Artificial Intelligence technologies can help English learners facilitate their learning in different learning stages, so that they can learn English in a personalized, immersive, flexible and smart way.Finally, there will be a conclusion about the application of the AI technologies in English education.
A Systematic Review of the Future of Education in Perspective of Block Chain

Blockchain is one of the most revolutionary technologies in the past decade due to its decentralisation, data integrity, reliability, and security. Blockchain technology is the next popular topic, and it has the potential to significantly alter the educational environment in many ways. Blockchain technology must be used in the education sector despite its challenges. Education is one of the sectors where blockchain-based solutions are still in use. Many academics possess extensive knowledge of the societal benefits blockchain technology might bring. The vast potential of blockchain can only be realised if education expands its knowledge of the technology. The primary goal of this research is to identify current problems related to educational institutions and identify blockchain features that could assist in addressing them. This research article will provide an overview of existing activities and address several perspectives on how blockchain can revolutionize the education sector. In prolongation, this article investigates the categories of blockchain technology applications, especially in the field of education. An in-depth discussion on the benefits and impact that blockchain brings to education is explored. Further, deliberate the abundant challenges of adopting blockchain in education. This study will direct the organizations/institutions to decide which blockchain application will benefit the most based on their requisites. The analysis will also provide information about other educational fields that may benefit from blockchain technology.
ActiveZero++: Mixed Domain Learning Stereo and Confidence-Based Depth Completion With Zero Annotation

Learning-based stereo methods usually require a large scale dataset with depth, however obtaining accurate depth in the real domain is difficult, but groundtruth depth is readily available in the simulation domain. In this paper we propose a new framework, ActiveZero++, which is a mixed domain learning solution for active stereovision systems that requires no real world depth annotation. In the simulation domain, we use a combination of supervised disparity loss and self-supervised loss on a shape primitives dataset. By contrast, in the real domain, we only use self-supervised loss on a dataset that is out-of-distribution from either training simulation data or test real data. To improve the robustness and accuracy of our reprojection loss in hard-to-perceive regions, our method introduces a novel self-supervised loss called temporal IR reprojection. Further, we propose the confidence-based depth completion module, which uses the confidence from the stereo network to identify and improve erroneous areas in depth prediction through depth-normal consistency. Extensive qualitative and quantitative evaluations on real-world data demonstrate state-of-the-art results that can even outperform a commercial depth sensor. Furthermore, our method can significantly narrow the Sim2Real domain gap of depth maps for state-of-the-art learning based 6D pose estimation algorithms.
Detecting Forged Receipts with Domain-Specific Ontology-Based Entities &amp; Relations

In this paper, we tackle the task of document fraud detection. We consider that this task can be addressed with natural language processing techniques. We treat it as a regression-based approach, by taking advantage of a pre-trained language model in order to represent the textual content, and by enriching the representation with domain-specific ontology-based entities and relations. We emulate an entity-based approach by comparing different types of input: raw text, extracted entities and a triple-based reformulation of the document content. For our experimental setup, we utilize the single freely available dataset of forged receipts, and we provide a deep analysis of our results in regard to the efficiency of our methods. Our findings show interesting correlations between the types of ontology relations (e.g., has_address, amounts_to), types of entities (product, company, etc.) and the performance of a regression-based language model that could help to study the transfer learning from natural language processing (NLP) methods to boost the performance of existing fraud detection systems.
ERACMA: Expressive and Revocable Access Control With Multi-Authority for AIoT-Enabled Human Centric Consumer Electronics

With the maturation of Artificial Intelligence empowered Internet of Things (AIoT) and smart consumer electronics technology, Securing data privacy has become a top priority concern. The lack of computing resources in some consumer electronics (e.g. wearable medical devices) and the inherent security issues such as untrustworthy cloud service providers (CSPs) inevitably lead to unreliable data sharing. Therefore, how to ensure the security, efficiency and scalability of private data sharing with limited resources in consumer electronics is one of the hot topics. However, data encryption schemes that use a single authority typically have high key generation costs, and the data integrity of which can hardly be guaranteed. In this paper, we propose a blockchain-assisted, highly secure access control scheme for AIoT-enabled, human centric consumer electronics. The novel scheme, called ERACMA, supports expressive and revocable access control with multi-authority using attribute-based encryption. The scheme uses multiple attribute authorities to split the key generation, reducing the generation time of the decryption key. Blockchain is utilized to ensure the integrity of private consumer electronics data. Data source identification can be used by human-centric consumer electronics applications to determine the validity and legitimacy of data. The scheme also supports dynamic user group management to address the user license expiration issues involved in consumer applications. A formal definition and concrete construction of ERACMA are also presented, with a security proof under the random oracle. Extensive comparative and experimental analysis has revealed the superior functionality and efficient performance of our scheme which is well-suited for human-centric consumer electronics.
A bibliometric and content analysis discourse on business application of blockchain technology

Purpose The article aims to provide a bibliometric and content analysis of the business application of blockchain technology. Design/methodology/approach Bibliometric analysis and content analysis were undertaken to accomplish the stated objectives of the study. Findings The results revealed publication and country collaboration trends, declining, emerging, and niche research areas, the topmost influential articles, authors, and most relevant journals. Originality/value As researchers have been myopic towards some mainstream research agendas studying the specific contexts of blockchain applications, this research believes a holistic approach to understanding blockchain technology's versatility to disrupt different business processes across a myriad of industries is lacking. In addition, to strengthen the research's conclusions, a content analysis of the most significant articles is conducted as part of the study.
Quantum Microwave Parametric Interferometer

Interferometers are extremely powerful tools for precision measurements in a plethora of research fields and applications, such as the detection of gravitational waves. The authors present experimental realization of a nonlinear microwave interferometer based on superconducting quantum circuits. Useful properties of this device range from a signal-to-noise ratio that exceeds the shot-noise limit, to sub-Poissonian intensity fluctuations between its outputs. These intriguing findings will promote applications ranging from quantum illumination to the search for axionic dark matter.
Fully Private Grouped Matrix Multiplication with Colluding Workers

In this paper, we present a novel variation of the coded matrix multiplication problem which we refer to as fully private grouped matrix multiplication (FPGMM). In FPGMM, a master wants to compute a group of matrix products between two matrix libraries that can be accessed by all workers while ensuring that any number of prescribed colluding workers learn nothing about which matrix products the master desires, nor the number of matrix products. We present an achievable scheme using a variant of Cross-Subspace Alignment (CSA) codes that offers flexibility in communication and computation cost. Additionally, we demonstrate how our scheme can outperform naive applications of schemes used in a related privacy focused coded matrix multiplication problem.
Evolved Part Masking for Self-Supervised Learning

Existing Masked Image Modeling methods apply fixed mask patterns to guide the self-supervised training. As those patterns resort to different criteria to mask local regions, sticking to a fixed pattern leads to limited vision cues modeling capability. This paper proposes an evolved part-based masking to pursue more general visual cues modeling in self-supervised learning. Our method is based on an adaptive part partition module, which leverages the vision model being trained to construct a part graph, and partitions parts with graph cut. The accuracy of partitioned parts is on par with the capability of the pretrained model, leading to evolved mask patterns at different training stages. It generates simple patterns at the initial training stage to learn low-level visual cues, which hence evolves to eliminate accurate object parts to reinforce the learning of object semantics and contexts. Our method does not require extra pretrained models or annotations, and effectively ensures the training efficiency by evolving the training difficulty. Experiment results show that it substantially boosts the performance on various tasks including image classification, object detection, and semantic segmentation. For example, it outperforms the recent MAE by 0.69% on imageNet-1K classification and 1.61% on ADE20K segmentation with the same training epochs.
Fundamental Limits of Multiple Sequence Reconstruction from Substrings

The problem of reconstructing a sequence from the set of its length-k substrings has received considerable attention due to its various applications in genomics. We study an uncoded version of this problem where multiple random sources are to be simultaneously reconstructed from the union of their k-mer sets. We consider an asymptotic regime where m = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> i.i.d. source sequences of length n are to be reconstructed from the set of their substrings of length k = β logn, and seek to characterize the (α,β) pairs for which reconstruction is information-theoretically feasible. We show that, as n →∞ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</inf> + 1, the source sequences can be reconstructed if β > max(2α + 1, α + 2) and cannot be reconstructed if $\beta < \max \left( {2\alpha + 1,\alpha + \frac{3}{2}} \right)$, characterizing the feasibility region almost completely. Interestingly, our result shows that there are feasible (α,β) pairs where repeats across the source strings abound, and non-trivial reconstruction algorithms are needed to achieve the fundamental limit.
Jedi: Entropy-Based Localization and Removal of Adversarial Patches

Real-world adversarial physical patches were shown to be successful in compromising state-of-the-art models in a variety of computer vision applications. Existing defenses that are based on either input gradient or features analysis have been compromised by recent GAN-based attacks that generate naturalistic patches. In this paper, we propose Jedi, a new defense against adversarial patches that is resilient to realistic patch attacks. Jedi tackles the patch localization problem from an information theory perspective; leverages two new ideas: (1) it improves the identification of potential patch regions using entropy analysis: we show that the entropy of adversarial patches is high, even in naturalistic patches; and (2) it improves the localization of adversarial patches, using an autoencoder that is able to complete patch regions from high entropy kernels. Jedi achieves high-precision adversarial patch localization, which we show is critical to successfully repair the images. Since Jedi relies on an input entropy analysis, it is model-agnostic, and can be applied on pre-trained off-the-shelf models without changes to the training or inference of the protected models. Jedi detects on average 90% of adversarial patches across different benchmarks and recovers up to 94% of successful patch attacks (Compared to 75% and 65% for LGS and Jujutsu, respectively).
Deep Learning-Driven Multi-Criteria Decision-Making for Effective Recommender Systems

Recommender systems are extensively employed across various domains, encompassing movies, hotels, and restaurants, with the aim of delivering personalized recommendations to users.However, the conventional approach of relying solely on a single rating may fall short in comprehending the underlying factors contributing to a user's overall rating.As a remedy, multi-criteria recommender systems (MCRSs) have emerged as an alternative paradigm, allowing users to rate items based on multiple dimensions.The primary challenge lies in predicting the overall rating considering a user's multi-criteria scores.In this study, we propose a MCRS that leverages a multicriteria decision-making method, which takes into account the interdependence among criteria.To this end, the weight assigned to each criterion is calculated using the correlation coefficient and standard deviation (CCSD) approach.Furthermore, we employ a deep autoencoder to capture intricate nonlinear relationships and generate recommendations of heightened accuracy.Evaluating our proposed method on the well-known MovieLens and TripAdvisor datasets, we demonstrate its superior performance compared to several single and multicriteria recommender systems.The results highlight the effectiveness of our approach in capturing pertinent aspects and achieving lower prediction errors, showcasing its potential in enhancing recommendation quality and personalization.
A New Model: Commutative Hypercomplex – Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

The purpose of the paper is to classify the Leukemia images.In this paper, we proposed a commutative model of a convolutional neural network for Leukemia image classification.We employ commutative hypercomplex modeling A[+1, -1] and A[-1, +1] to build the new model.We hire an augmentation model to enrich the image data sets for the training sets through rotation, zooming, and flipping.We evaluated our proposed method using acute lymphoblastic leukemia image database type 2 (ALL-IDB2).The results show that our proposed method has delivered the best average accuracy at 96.43% for A[+1,-1] and 97.05 for A[-1,+1].We have measured and found maximum accuracy at 100% for A[+1,-1] and A[-1,+1].Comparison results show that our proposed method outperformed Knearest neighbor, support vector machine-radial basis function, support vector machine-linear, support vector machinepolynomial, naïve bayes gaussian, naïve bayes complement, decision tree, and colour hybrid modelling.
Towards “Image Reflow” on the Web: Avoiding Excessive Panning of Magnified Images by Multiplexing Automatically Cropped Regions of Interest

Low vision users are often faced with large images when increasing the browser zoom level. Large images extending beyond the viewport are difficult to view due to the excessive two-dimensional panning involved. This demo presents a mechanism that automatically splits, or “reflows”, images into regions of interest. The user cycles through the magnified regions of interest by clicking or tapping the images.
Influence of Convolutional Neural Network Depth on the Efficacy of Automated Breast Cancer Screening Systems

Breast cancer is a global health concern for women. The detection of breast cancer in its early stages is crucial, and screening mammography serves as a vital leading-edge tool for achieving this goal. In this study, we explored the effectiveness of Resnet 50v2 and Resnet 152v2 deep learning models for classifying mammograms using EMBED datasets for the first time. We preprocessed the datasets and utilized various techniques to enhance the performance of the models. Our results suggest that the choice of model architecture depends on the dataset used, with ResNet152 outperforming ResNet50 in terms of recall score. These findings have implications for cancer screening, where recall is an important metric. Our research highlights the potential of deep learning to improve breast cancer classification and underscores the importance of selecting the appropriate model architecture.
Finding Camouflaged Objects along the Camouflage Mechanisms

Common mechanisms for achieving object camouflage include reducing differences and increasing distractions. Such camouflage mechanisms hinder the object detectors to accurately distinguish the camouflaged objects from their surroundings. Considering that, we reexamine the camouflaged object detection (COD) task from the perspective of camouflage mechanisms and make the first attempt to discover the target objects in a de-camouflaging manner. We argue that this process can not only lead to a better understanding of camouflage, but also provide a new perspective for detecting camouflaged objects. For that, we first analyze some existing camouflage mechanisms together with their induced problems. Afterwards, considering the inner relationships between SOD and COD, we resort to the SOD task to synergistically achieve de-camouflaging for COD. Specifically, we incorporate the SOD task into the COD model and present a multi-task learning framework for COD, which models the intrinsic relationships between the two tasks from different perspectives, i.e., task-conflicting attribute and task-consistent attribute, to destroy the camouflage conditions for highlighting those inconspicuous yet valuable cues of camouflaged objects. In more detail, modeling the task-conflicting attribute is to well identify camouflaged objects by alleviating such interfering information from salient ones, and is achieved by a Gate Classification (GC) strategy and a Region Distraction Module (RDM). While, modeling the task-consistent attribute, which is achieved by an adversarial learning (AL) scheme and a Boundary Injection Module (BIM), is intended to enhance the boundary differences between the camouflaged objects and their backgrounds for fully segmenting the camouflaged objects. Extensive results demonstrate the superiorities of our proposed model over existing ones in camouflaged object detection.
Large-scale orthogonal integer wavelet transform features-based active support vector machine for multi-class face recognition

Support vector machines are widely utilised in the field of Face Recognition (FR) but it suffers from the drawback of high-computational time. In proposed work, new active set strategy is utilised for support vector machines on Integer Wavelet Transform (IWT) based large scale facial features with low-computational time. Lifting scheme-based significant localised wavelet features are extracted using IWT based on orthogonal wavelets. Large Scale Orthogonal-IWT (LSOI) features with maximum covariance are then projected into eigen space from where robust training and testing features are selected. For classification of data, Active Support Vector Machine (ASVM) based machine learning technique is utilised which generates a less complex procedure compared to traditional support vector machine. ASVM aims to solve a fixed number of linear equations for One-vs-One (OVO) and One-vs-All (OVA) multiclass FR. Extensive experiments on Yale, ORL, AR, JAFFE and Georgia-Tech databases have revealed high performance compared to existing FR techniques.