Reviewerly/exHarmony huggingface dataset:
New challenges in mitigating climate change: Digital teaching for the sustainable development and innovation

The phenomenon of climate change has become a global challenge that affects human activities in many ways. Exploring the sustainability and innovativeness of digital education is an important reference for the further implementation of science and education strategies and positive effects on climate change mitigation. This study designed two questionnaires for basic and tertiary education to address variability in education and collected 523 samples for basic education and 412 samples for tertiary education respectively. Using digital teaching practices and digital teaching quality as mediators, structural equation modelling (SEM) was used to examine the impact of digital education on sustainability and innovation in education. Research shows that digital teaching and learning in basic and higher education can significantly contribute to the two-way development of educational sustainability and innovation, through digital teaching and learning practices. Digital teaching practice and digital teaching quality in higher education play a complete mediating role, while basic education only plays a partial mediating role. Higher education emphasizes digital teaching and learning practice processes and teaching quality, and most students hope that online teaching and learning can be further integrated with offline education to form a new model of education. This study will help government departments understand the pedagogical reality of digital education at a deeper level and provide ideas for the subsequent sustainable development and educational innovation of digital teaching. It can also provide new ideas for climate change mitigation and sustainable development.
Simulation of Acoustic Characteristics in the Vector-Phase Field of the Horn at Low Frequencies

Methods of computer technology are widely used in modern acoustics.They are used to visualize the structures of wave fields, when the geometry of the boundaries and the physical properties of the region where the waves propagate are known.When using these methods, the location of the emitters and receivers is usually controlled.At present, known methods based on the use of information recorded only by pressure transducers have reached the limit.Vector-phase characteristics methods have previously been used to study the spatial distribution of the main characteristics of sound and infrasonic fields.But to study the structure of the acoustic field in the speakers of finite length, they have not been used so far.This is the relevance of the chosen topic.In this work the research of acoustic characteristics of a catenoid loudspeaker at low frequencies and a vector -phase field of a loudspeaker by numerical methods is given.The following tasks are solved: compiling an analytical review of research methods of sound fields; description of the mathematical apparatus of wave propagation in the catenoid mouthpiece; simulation of acoustic characteristics in the vector -phase field of the loudspeaker at low frequencies in ANSYS.In loudspeakers, nonlinear distortions associated with the heterogeneity of the magnetic field in the gap of the magnetic circuit and, in violation of Hooke's law, are not so significant.The defining moment in the development of the theory of wave radiation by loudspeakers was the publication of Webster's work, in which the wave equation is given.Although the membrane performs a simple harmonic oscillation.The problem of amplifying the action and direction of the acoustic signal was solved, during which a catenoid horn was chosen.The propagation of the wave is no longer sinusoidal, and contains in addition to the main harmonics of higher orders.That is, nonlinear effects inside the speaker are detected.Previously, the design of sound generators did not provide structural elements that reduce or eliminate completely nonlinear effects in the speaker, which reduced the efficiency of generating the sound of the fundamental tone.The solution of the problem of modeling the process of propagation of the second harmonic along the horn is considered.In order to calculate the acoustic characteristics in the cavity of the catenoid loudspeaker, it is necessary in ANSYS CAD.
Deep Learning-Based Recommender Systems—A Systematic Review and Future Perspective

With the ever-increasing volume of online information, recommender systems (RS) have been an effective technique to overcome such information overload. Given its widespread implementation in a variety of web applications, the value of RS cannot be emphasized enough. RS has the potential to improve numerous issues identified with over-choice. Deep learning's influence is also widespread, with new evidence of its efficacy in information retrieval and RS research. In RS, the field of deep learning is emerging. This article presents a thorough evaluation of recent research findings on traditional RS and deep learning-based recommender systems (DLRS). This review covers articles published between 2018 and 2022 in four major research databases: Science Direct, IEEE Explore, Springer, and Wiley. First, we provide a complete overview, comparison, and summary of traditional RS. We then systematically analyze DLRS challenges and related solution approaches. Finally, under the discussion of open issues at DLRS, we highlight possible research directions in this area. In this review, we report quantitative data from previous studies to highlight comparisons based on metrics such as F1-score accuracy, recall, accuracy, and error functions.
Detection of Suspicious Human Activities from Surveillance Camera Using Neural Networks

Constant data monitoring by humans to determine if events are anomalous is a near-impossible undertaking that necessitates a crew and their undivided attention. Many organizations have installed CCTV cameras that record video and store it on a centralized server so that individuals and their interactions may be monitored at all times. The requirement for automatic systems to detect and characterize suspicious actions caused by objects is growing as the amount of video data acquired everyday by surveillance cameras grows. It’s important to show which frame and which part of it contains the odd activity so that the unexpected activity can be judged as abnormal or suspicious more quickly. The main task is to follow these moving items through the visual series. This is accomplished by turning video into frames and assessing the people and their activities within those frames.
High Utility Itemset Mining and Inventory Management: Theory and Use Cases

High utility itemset mining is a recent trend of finding not the most frequent items sold in the store, but finding the items sold of high utility to the store in terms of price and quantity. The knowledge gained from high utility itemset mining can be utilized in multiple ways for managing the inventory of a store. This paper envisions possible use cases of high utility itemset mining to inventory management. The use cases of this paper are based on a few synthetic examples and a real-world dataset in the retail domain. The motivation of the paper is to broaden the horizon by suggesting a few possible uses of high utility itemset mining in inventory management.
Modified Elephant Herd Optimization-Based Advanced Encryption Standard

Cryptography is commonly employed to ensure secure data transfer via unsecure communication networks. With the rising need for picture transmission confidentiality and privacy, an efficient encryption approach becomes vital. The architectural flow of the newly created technique is implied in the proposal. Based on Cryptographic Method for Digital Picture Security, the design of an efficient Modified Elephant Herd Optimization-based Advanced Encryption Standard (MEHO-AES) is presented. The suggested MEHO-AES approach employs multilevel discrete Cosine transform (DCT) for image decomposition, with the input picture divided into RGB components for examining the fundamental colors of all image sections. Moreover, the AES encryption technology is used throughout the encryption procedure. Additionally, the MEHO method is used to determine the best encryption keys.
Efficient Data Security Using Hybrid RSA-TWOFISH Encryption Technique on Cloud Computing

In recent times, whichever technology can’t be specified faultlessly up to its permitted susceptibility. Several skills have grown up in regular life that proposes accessible data storing, actuality capable to contact the data after everywhere every time you need and utmost essentially, they deliver on the Internet use some software. Cloud computing is an example given that you through data storing and too it owns dissimilar facilities on let convention ability. In the context of cloud computing cloud safety is a big worry. To protect the cloud environment, several investigation mechanisms are being planned. Cryptography is used to overwhelm the safety concern and attain the CIA’s possessions. Cryptography is the great equalizer of data transmission and storing safety it is an identical and valuable method to safeguard. Customary symmetric and asymmetric have certain restrictions. To resolve this, we are successful to present a fresh hybrid method to attain extraordinary data safety and privacy. In this paper, we are examining RSA and Twofish to appliance a hybrid procedure. The presentation of the hybrid method is equated through the current hybrid technique and displays that the planned technique delivers high safety and privacy of patient data. To overthrow the problems of together symmetric and asymmetric, hybrid cryptography is used.
User Story-Based Automatic Keyword Extraction Using Algorithms and Analysis

Writing effective requirement specification documents in the face of dynamic user needs is a significant challenge in modern software development. Keyword extraction algorithms can help to retrieve relevant information from functional requirements provided by users. The process of identifying a concise set of words that capture the essence of user stories without losing important information is accomplished through automatic keyword extraction. This paper presents a comparative study of four popular algorithms for keyword extraction: Rapid Automatic Keyword Extraction (RAKE), Yet Another Keyword Extraction (YAKE), TextRank, and KeyBERT. The algorithms are analyzed based on their ability to extract keywords and provide corresponding scores. Additionally, N-gram analysis is performed using the extracted keywords. The study concludes that the RAKE algorithm exhibits better performance in extracting relevant keywords from user stories compared to the other algorithms.
The Potential of Using Corpora and Concordance Tools for Language Learning: A Case Study of ‘Interested in (Doing)’ and ‘Interested to (Do)’

This study uses corpus resources to investigate the rules of usage of two closely related expressions in English—‘interested in (doing)’ and ‘interested to (do)’—in order to assess the potential for language learning. Our investigation developed in three stages—(a) gaining a general view of the trends of use of the two expressions, b) identifying the verbs that construct the two, and c) detecting the rules of usage by scrutiny of the available evidence. Our results attest that the corpus technology provides readily useful information in the first two stages, while in-depth human–computer interaction (HCI) is crucial for inquisitive language learners to discover the rules in the third stage. The process of discovery not only takes multiple stages but also requires intensive human intelligence. We discuss the model for HCI as a focus for future studies in the context of the new generation of AI such as ChatGPT.
Data Integrity Protection Using Multi-level Reconstructive Error Data and Auditing for Cloud Storage

Cloud storage services offer all the users/clients to save any huge quantity of data and to access the data anytime from anywhere. Clients can edit or view their data anytime, but costing may be valid for whichever data they are using. In this paper, we introduce system to enable data replication, integrity checking, and data recovery on cloud data. This approach may increase the data integrity in the cloud-based storage. A hybrid approach using replication along with coding technique is used to tackle the reconstruction. Erasure coding of data is coupled with extra parity bits to reduce the reconstruction cost. This enables a reduction in reconstruction time while maintaining a reasonable storage overhead. Overall, our novel approach in the cloud storage is cost-effective as well as increasing the data integrity protection in the cloud storage.
Surveillance Video-Based Object Detection by Feature Extraction and Classification Using Deep Learning Architecture

As of late, deep learning has accomplished top exhibitions in object recognition undertakings. Be that as it may, continuously, frameworks having memory or processing restrictions extremely wide and profound organizations with various boundaries comprise a significant impediment. Profound gaining-based object location arrangements arose out of PC vision has spellbound undivided focus as of late. This examination proposes novel method in observation video-based object location by highlight extraction with characterization utilizing profound learning. Here the info information has been gathered as observation video and handled for commotion expulsion, smoothening, standardization. Then, at that point, the handled video has been separated and ordered utilizing concealed convolution fluffy perception brain organizations. The exploratory examination has been completed as far as exactness, accuracy, review, F-1 score, RMSE, NSE. Proposed method attained accuracy of 91%, accuracy of 84%, review of 86%, F-1 score of 76%, RMSE of 61%, and NSE of 48%.
Surface Electromyography Assisted Hand Gesture Recognition Using Bidirectional LSTM and Unidirectional LSTM for the Hearing Impaired

Sign Recognition System (SRS) is a technology that enables efficient communication between the deaf-mute community and the rest of society. Recent developments in machine learning and biomedical sensor technology have led to improvements in sign recognition systems. In this paper, we propose a sign recognition model that uses sensor data from a surface electromyography (sEMG) device and recognizes particular signs using a Stacked Bidirectional and Unidirectional LSTM (SBUL) architecture. The proposed model has significant social impact, as it facilitates communication among the deaf-mute community by recognizing signs using sEMG data. The neural network architecture proposed in this paper improves the performance of the model by considering temporal data dependencies between sEMG data and the layers of the proposed LSTM. This model provides information on temporal sequential data and offers an efficient method for SRS. Experimental results using sensor data demonstrate that the proposed SBUL network for sign recognition has an average recognition rate of approximately 96%. The model based on SBUL and sEMG provides better accuracy and results for solving the problem of sign recognition.
Classifying Human Activities Using Machine Learning and Deep Learning Techniques

The ability of machines to recognize and categorize human activities is known as human activity recognition (HAR). Most individuals today are health aware; thus, they use smartphones or smartwatches to track their daily activities to stay healthy. Kaggle held a challenge to classify six human activities using smartphone inertial signals from 30 participants. HAR’s key difficulty is distinguishing human activities using data so they do not overlap. Expert-generated features are visualized using t-SNE, then logistic regression, linear SVM, kernel SVM, and decision trees are used to categorize the six human activities. Deep learning algorithms of LSTM, bidirectional LSTM, RNN, and GRU are also trained using raw time series data. These models are assessed using accuracy, confusion matrix, precision, and recall. Empirical findings demonstrated that the linear support vector machine (SVM) in the realm of machine learning, as well as the gated recurrent unit (GRU) in deep learning, obtained higher accuracy for human activity recognition.
Nucleus Segmentation Using Adaptive Thresholding for Analysis of Blood and Bone Marrow Smear Images

The analysis of blood and bone marrow smear pictures aids in the detection of hematological malignancies. The colour levels of the nucleus and its neighbours in these smear images aid in identifying cell malignancy. The primary goal of this project is to conduct colour-based segmentation on blood/bone marrow smear images. These photos are first treated to contrast stretching using the dark contrast algorithm (DCA). This enhancement boosts the contrast of the nucleus and its surrounding components. Using an adaptive thresholding approach, colour-based segmentation of enhanced microscopic pictures (blood/bone marrow smear images) was performed. The colour segmented image is then transformed to binary to create the binary mask, which is then evaluated using the overlap ratio (OR) to determine performance.
Make Spoken Document Readable: Leveraging Graph Attention Networks for Chinese Document-Level Spoken-to-Written Simplification

As people use language differently when speaking compared to writing, transcriptions generated by automatic speech recognition systems can be difficult to read. While techniques exist to simplify spoken language into written language at the sentence level, research on simplifying spoken language has various spoken language issues at the document level is limited. Document-level spoken-to-written simplification faces challenges posed by cross-sentence transformations and the long dependencies of spoken documents. This paper proposes a new method called G-DSWS (Graph attention networks for Document-level Spoken-to-Written Simplification) using graph attention networks to model the structure of a document explicitly. G-DSWS utilizes structural information from the document to improve the document modeling capability of the encoder-decoder architecture. Experiments on the internal and publicly available datasets demonstrate the effectiveness of the proposed model. And the human evaluation and case study show that G-DSWS indeed improves spoken Chinese documents' readability.
Feature-Fusion-Based Haze Recognition in Endoscopic Images

Haze generated during endoscopic surgeries significantly obstructs the surgeon's field of view, leading to inaccurate clinical judgments and elevated surgical risks. Identifying whether endoscopic images contain haze is essential for dehazing. However, existing haze image classification approaches usually concentrate on natural images, showing inferior performance when applied to endoscopic images. To address this issue, an effective haze recognition method specifically designed for endoscopic images is proposed. This paper innovatively employs three kinds of features (i.e., color, edge, and dark channel), which are selected based on the unique characteristics of endoscopic haze images. These features are then fused and inputted into a Support Vector Machine (SVM) classifier. Evaluated on clinical endoscopic images, our method demonstrates superior performance: (Accuracy: 98.67%, Precision: 98.03%, and Recall: 99.33%), outperforming existing methods. The proposed method is expected to enhance the performance of future dehazing algorithms in endoscopic images, potentially improving surgical accuracy and reducing surgical risks.
A Deep Joint Model of Multi-scale Intent-Slots Interaction with Second-Order Gate for SLU

Slot filling and intent detection are crucial tasks of Spoken Language Understanding (SLU). However, most existing joint models establish shallow connections between intent and slot by sharing parameters, which cannot fully utilize their rich interaction information. Meanwhile, the character and word fusion methods used in the Chinese SLU simply combines the initial information without appropriate guidance, making it easy to introduce a large amount of noisy information. In this paper, we propose a deep joint model of Multi-Scale intent-slots Interaction with Second-Order Gate for Chinese SLU (MSIM-SOG). The model consists of two main modules: (1) the Multi-Scale intent-slots Interaction Module (MSIM), which enables cyclic updating the multi-scale information to achieve deep bi-directional interaction of intent and slots; (2) the Second-Order Gate Module (SOG), which controls the propagation of valuable information through the gate with second-order weights, reduces the noise information of fusion, accelerates model convergence, and alleviates model overfitting. Experiments on two public datasets demonstrate that our model outperforms the baseline and achieves state-of-the-art performance compared to previous models.
Fooling Downstream Classifiers via Attacking Contrastive Learning Pre-trained Models

Nowadays, downloading a pre-trained contrastive learning (CL) encoder for feature extraction has become an emerging trend in computer vision tasks. However, few works pay attention to the security of downstream tasks when the upstream CL encoder is attacked by adversarial examples. In this paper, we propose an adversarial attack against a pre-trained CL encoder, aiming to fool the downstream classification tasks under black-box cases. To this end, we design a feature similarity loss function and optimize it to enlarge the feature difference between clean images and adversarial examples. Since the adversarial example forces the CL encoder to output distorted features at the last layer, it successfully fools the downstream classifiers which are heavily relied on the encoder's feature output. Experimental results on three typical pre-trained CL models and three downstream classifiers show that our attack has achieved much higher attack success rates than the state-of-the-arts, especially when attacking the linear classifier.
An Alignment and Matching Network with Hierarchical Visual Features for Multimodal Named Entity and Relation Extraction

In this paper, we study the tasks of multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), both of which involve incorporating visual modality to complement text modality. The core issues are how to bridge the modality gap and reduce modality noise. To address the first issue, we introduce an image-text alignment (ITA) module to obtain a better unimodal representation by aligning the inconsistent representations between image and text, which come from different encoders. To tackle the second issue, we propose an image-text matching (ITM) module that constructs hard negatives to improve the model's ability to capture the semantic correspondence between text and image. Besides, we also selectively combine and concatenate the hierarchical visual features obtained from both global and visual objects of Vision Transformer (ViT) as improved visual prefix for modality fusion. We conduct extensive experiments to demonstrate the effectiveness of our method (AMNet) (Code is available in https://github.com/Grident/AMNet ) and achieve state-of-the-art performance on three benchmark datasets.