Reviewerly/exHarmony huggingface dataset:
Designing a virtual reality-support for the thesis supervision meetings: A case of a Sino-British international university in China

Thesis supervision is a crucial teaching activity in higher education that plays a vital role in assisting students in completing their studies. The outbreak of COVID-19 has necessitated the transitionto remote, synchronous interactions for supervising academic papers. However, existing tools for conducting these remote sessions often fall short of meeting the requirements of both teachers and students. This paper focuses on an investigation into the utilization of virtual reality (VR) technology for conducting remote thesis supervision meetings at Xi'an Jiaotong-Liverpool University (XJTLU), a Sino-British institution located in Suzhou, China. Through a year-long qualitative inquiry involving two teachers and five students who participated in the project both in Liverpool and Suzhou, this research examines how actual collaborative work is conducted during remote supervision via a research-developed tool. The results of this study emphasize the need for improvements in the current design to better align with the practical requirements of teachers and students during such meetings. Furthermore, this research provides insights into the design considerations for VR applications tailored to specific use cases and offers a set of implementation details to guide the development of VR-based remote collaboration tools for thesis supervision meetings.
量子Fisher信息流及其应用

量子Fisher信息作为精密测量领域中的重要概念, 其倒数给出被测参数可达精度的上限。本文利用右对数导数给出开放量子系统量子Fisher信息流的表达式, 并将其应用于与非马尔可夫环境相互作用的二能级系统, 讨论了利用右对数导数给出的量子Fisher信息流的正负性与非马尔可夫过程之间的关系。此外, 系统在演化的过程中, 量子Fisher信息流存在奇异性, 该奇异性与量子Fisher信息的奇异性同时出现, 它们均来源于密度矩阵某一趋于零的本征值。
Camouflage target detection based on strong semantic information and feature fusion

Aiming at the detection difficulties in camouflage target detection, such as the high similarity between the target and its background, serious damage to the edge, and strong concealment of the target, a camouflage target detection algorithm YOLO of camouflage object detection based on strong semantic information and feature fusion is proposed. First, the attention mechanism convolutional block attention module (CBAM) is constructed to highlight the important channel features and target spatial locations to further aggregate rich semantic information from the high-level feature map. Then the atrous spatial pyramid pooling module is constructed to repeatedly sample the multiscale feature maps to expand the receptive field of the neural network, reduce feature sparsity in the process of convolution, and ensure dense features and multiscale contextual semantic information enter the feature fusion module. Finally, the attention skip-connections are constructed based on the CBAM module for fusing the original feature maps extracted by the backbone network to the corresponding detection outputs so as to eliminate the redundant features as well as enrich the target information of the network outputs. In order to fully verify the performance of the proposed algorithm, a camouflage target detection dataset named strong camouflage efficiency target dataset (SCETD) is constructed. Experimental results on SCETD show that the precision and recall of the proposed algorithm achieve 96.1% and 87.1%, respectively. The AP0.5 and AP0.5 : 0.95 achieve 92.3% and 54.4%, respectively. The experimental results prove the effectiveness of the proposed method in detecting camouflage targets.
Examining Schnorrs protocol in the context of zero-knowledge proofs

The rise of technology has brought with it a heightened awareness of the necessity to shield personal data and maintain exclusive access to specific knowledge. A notable solution that emerged from this consciousness is Zero-Knowledge Proofs (ZKPs) and, more specifically, Schnorrs Protocol. Historically, Zero-Knowledge Proofs have a compelling lineage, tracing their roots back to the fervent discussions among cryptographers aiming to achieve a balance between information sharing and privacy. ZKPs are cryptographic methods that allow one party to prove to another that a statement is true, without revealing any specific information about the statement itself. In the midst of these developments, Schnorrs Protocol emerged as a renowned interactive proof system. It possesses an intuitive structure that has made it pivotal in the enhancement of digital security. The typical flow of Schnorrs Protocol begins with the prover sending a commitment to the verifier. The verifier then sends a random challenge back to the prover, who, in turn, produces a response. Whats captivating is that the verifier can ascertain the validity of the proof without gaining insight into the underlying secret. Interactive Schnorrs Protocol involves real-time back-and-forth communication between the prover and verifier. On the other hand, the non-interactive version eliminates this need by using a cryptographic hash function, thereby streamlining the process.
Topic-Focused Introduction to R and Data sets Used

The theoretical concepts explained in the book are illustrated by examples, which make use of the statistical software environment R. In this chapter, a short introduction to some functionalities of R is given. This introduction does not replace a general introduction to R, but it provides the background that is necessary to understand the examples and the R code in the book. First, the available software tools are briefly discussed before the focused introduction to R is given by also introducing the package VIM, which is used throughout the book. Finally, some interesting data sets are introduced. Most of them are used for demonstration purposes and exercises in this book.
Hierarchical Access Structure-based Scheme with Outsourcing and Revocation Mechanism for Cloud Environment

Ciphertext policy attribute-based encryption (CPABE) is one of the efficient implementations of encrypted access control scheme for cloud computing. Though multiple implementations of CPABE exist, there are some issues that need to be addressed, including efficient revocation approach, decryption time, storage cost etc. In this paper, an efficient scheme that incorporates a hierarchical access structure, outsourced decryption, as well as user and attribute revocation is presented. The hierarchical access structure is utilized to encrypt multiple data using one common access structure and makes the encryption process more efficient. The outsourcing server is used to perform partial decryption, so that all heavy computations are performed by this server and less overhead is incurred by the data user. The proposed framework also integrates the evaluation of trustworthiness of data users and service providers to ensure trusted and encrypted access control procedures. The paper also presents an analysis of the time required for performing different operations. Simulation results show that the proposed scheme outperforms the existing approaches.
Tangent Search Long Short Term Memory with Aadaptive Reinforcement Transient Learning based Extractive and Abstractive Document Summarization

Text summarization is the process of creating a shorter version of a longer text document while retaining its most important information. There have been a number of methods proposed for text summarization, but the existing method does not provide better results and has a problem with sequence classification. To overcome these limitations, a tangent search long short term memory with adaptive reinforcement transient learning-based extractive and abstractive document summarization is proposed in this manuscript. In abstractive phase, the features of the extractive summary are extracted and then the optimal features are selected by Adaptive Flamingo Optimization (AFO). With these optimal features, the abstractive summary is generated. The proposed method is implemented in python. For extractive text summarization, the proposed method attains 42.11% ROUGE-1 Score, 23.55% ROUGE-2 score and 41.05% ROUGE-L score using Gigaword. Additionally, 57.13% ROUGE-1 Score, 28.35% ROUGE-2 score and 52.85% ROUGE-L score using DUC-2004 dataset. For abstractive text summarization the proposed method attains 47.05% ROUGE-1 Score, 22.02% ROUGE-2 score and 48.96% ROUGE-L score using Gigaword. Also, 35.13% ROUGE-1 Score, 20.35% ROUGE-2 score and 35.25% ROUGE-L score using DUC-2004 dataset.
Concept of Metaverse

Metaverse, a hypothetical internet iteration, is a concept that is new to the world. This chapter begins with defining the concept in detail followed by the requisite elements of the Metaverse. Considering the constant evolution in this area, it is important to understand the current state of development and innovation through the three main phases, viz., the emerging phase, the advanced Metaverse, and the mature Metaverse. Further, the Metaverse has become a game changer, especially after Covid-19 and has become a huge support to the economies. Its economic implications are crucial and need emphasis with a detailed assessment.
Way Forward For Metaverse Adoption

This is a concluding chapter that describes metaverse technology challenges posed on businesses as well as users. Ethical consideration is also a crucial point to discuss especially when the metaverse possesses difficulties such as cyberbullying, privacy and fairness and safety. Business implications for both consumers, as well as enterprises, discuss the opportunity landscape in creating end-to-end journeys immersive. In the end, the chapter highlights what can be done today to prepare for the future.
Seven Layers of Metaverse

Metaverse comprises seven fundamental layers dedicated to the framework of the structure, output, and ethical framework of an idealized decentralized metaverse. These seven layers are experience, discovery, creator economy, spatial computing, decentralization, human interface, and infrastructure. Each layer and its application have been discussed in detail to understand its contribution to the world of the metaverse.
Metaverse Platforms and Use-Cases

Currently, metaverse is experiencing its adoption by popular tech companies such as Apple, Facebook, Microsoft, Accenture, and Disney. Its use-cases can also be noticed in industries such as gaming, education, travel, tourism, healthcare, banking, and a prominent functional area like human resource management. Though it is still at the developing stage, companies from these industries are trying their best to pursue the best of what is available in the metaverse world. This chapter discusses the adoption of metaverse and its use-cases in companies and popular industries in the world.
Implementation of Early Reading Games to Teach English Using Integrated Sound Word Method for Preschoolers

In teaching English reading skills to preschoolers, two primary reading instruction approaches have been established: the Phonics Approach and the Whole Language Approach. However, no evidence was found in the past studies to support these methods in the Malaysian setting. This study examines the effects of early reading games on teaching English among second language learners in preschools using the Integrated Sound Word Method using 60 preschoolers aged six years old in the district of Kuala Lumpur as samples. Two groups were formed to be assigned as the control group and intervention group. Pre-test, post-test, and post-test 2 were administered to the control and intervention groups before and after the quasi-experimental study. The Integrated Sound Word Method incorporated early reading games as an intervention for the intervention group. This study used one-way analysis of variance (ANOVA) as well as the learning outcomes of the experimental study to measure its significance. The findings showed that the intervention group performed better in improving early reading proficiency through games as compared to the control group at each level. The participants in the intervention group are able to grasp early reading proficiency after learning through early reading games using the Integrated Sound Word Method. This study has several implications for educators and policymakers in developing effective reading programs for second language learners, emphasizing the significance of a balanced approach to achieving the objectives of the Malaysia Education Blueprint (2013–2025).
NLP and It’s all Application in AI

Natural Language Processing (NLP) plays a pivotal position in the evolution and enhancement of Artificial Intelligence (AI). This paper explores the symbiotic courting between NLP and AI, dropping mild at the essential techniques and models that underpin NLP. It delves into the big range of applications wherein NLP excels, from textual content category to language technology, emphasizing its pervasive presence in our day by day lives, which includes chatbots, digital assistants, and advice structures. The paper also addresses the inherent demanding situations and boundaries of NLP, along with biases in education statistics and privateness concerns, underscoring the want for moral and accountable AI improvement. Recent improvements, which include the upward push of large-scale pre-trained models, bring in new opportunities, whilst actual-global case research illustrate NLP's effect. Looking ahead, we speak the future tendencies in NLP, from improved language era to its role in healthcare and finance. This research paper underscores the fundamental role of NLP within the AI panorama and its capability to transform the way we interact with technology and facts.
THE EFFECT OF USING COLLABORATIVE STRATEGY READING ON STUDENTS READING COMPREHENSION IN NARRATIVE TEXT AT TENTH GRADE OF SMAN 1 TEMBILAHAN

This research aimed to know the effect of using Collaborative Strategy Reading on students reading comprehension at SMAN 1 Tembilahan. The researcher found several problem experianced by students in english, such as: Students had difficulty understanding reading texts, some students not able to get information from the text, students need much time to comprehend the text, and students have problems answering the reading passage. Collaborative Strategy Reading is useful for students skill in reading comprehension. The design of this research was the tenth grade of SMAN 1Tembilaha 2022/2023 academic year. The population of this research was 312 students consist of 9 classes. While, the sample of this research was two classes consist of 68 students. The sample of this class was X.MIPA5 as control group and X.MIPA4 as experimental class. To get the data of this research, the researcher gave test as insruments which included pre-test and post-test. For the pre-test and post-test in the form of multiple choices test, consist of 2o items questions for each. The learning prosess was three meetings. The finding of this research showed that the avarage score in pre-test was 38,5 while in post-test was 66,3 for the experimental group. The mean score of post-test was higher than the mean score of pre-test. The result of analysis, it showed that the value Tobserved 3,39 was higher than Ttable was 2,00 in significant 5% based on the result, it was provided that the null hypothesis (H0) was rejected and alternative hypothesis (Ha) was accepted. Therefore, there was a significant effect of Collaborative Strategy Reading on students reading comprehension at SMAN 1 Tembilahan
Exploratory Analysis of Decision-Making Biases of Professional Red Teamers in a Cyber-Attack Dataset

Attacker psychology is currently under-examined in cybersecurity research. A prior, large-scale study sought to understand attackers’ behavior by testing both technological and psychological deception. Professional “red team” members participated over two days in various conditions. This data was examined for further evidence that cognitive biases, a potential disruption for attackers, may be present, and may be affecting the outcome. An applied, novel methodology for measuring confirmation bias and framing effects is presented using this realistic dataset. Both confirmation bias and the framing effect occurred in this interpretation. The framing effect appears to have reduced attacker interactions with systems in the network, which may benefit cyber defenders. These results provide additional, exploratory evidence that biases in the decision-making of cyber attackers could be used as part of a defensive cyber strategy. Limitations to the approach and directions for future study of attackers are discussed.
Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

Abstract Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.
RWNR: Radial Basis Feed Forward Neural Network Driven Semantically Inclined Strategy for Web 3.0 Compliant News Recommendation

In this paper, we propose a framework for socially responsible and semantically guided Web 3.0 compatible news recommendation that combines deep learning with strategic knowledge derivation. For this reason, RWNR model has been proposed. The RWNR model encompasses the radial basis feed forward neural network which is transformed into a feature controlled model by encompassing entities from the news API stack as features to classify the dataset and the classified dataset is subjected to the computation of strategic semantic similarity as well as step deviance at several phases with the reference tag pool which is generated from the user’s topic of interest which is subjected to topic modelling utilizing Latent Dirichlet allocation (LDA) harvesting entities from wikidata as well as making it socially aware by encompassing the twitter API. Hybridization of entities from the news API stack is done from several knowledge derivation schemes into the model. The normalized information distance (NID) and tag index with the harmony search optimization algorithm is metaheuristic in nature which optimizes the solution that makes the proposed model not only rich in knowledge but also rich in semantics and optimization. This has resulted in best-in-class model with average recall, F-measure, False discovery rate (FDR), accuracy percentages of 96.09%, 91.19%, 96.64% and 96.63% respectively with the lowest FDR of 0.04.
Attentive multi-scale aggregation based action recognition and its application in power substation operation training

With the rapid development of the power system and increasing demand for intelligence, substation operation training has received more attention. Action recognition is a monitoring and analysis system based on computer vision and artificial intelligence technology that can automatically identify and track personnel actions in video frames. The system accurately identifies abnormal behaviors such as illegal operations and provides real-time feedback to trainers or surveillance systems. The commonly adopted strategy for action recognition is to first extract human skeletons from videos and then recognize the skeleton sequences. Although graph convolutional networks (GCN)-based skeleton-based recognition methods have achieved impressive performance, they operate in spatial dimensions and cannot accurately describe the dependence between different time intervals in the temporal dimension. Additionally, existing methods typically handle the temporal and spatial dimensions separately, lacking effective communication between them. To address these issues, we propose a skeleton-based method that aggregates convolutional information of different scales in the time dimension to form a new scale dimension. We also introduce a space-time-scale attention module that enables effective communication and weight generation between the three dimensions for prediction. Our proposed method is validated on public datasets NTU60 and NTU120, with experimental results verifying its effectiveness. For substation operation training, we built a real-time recognition system based on our proposed method. We collected over 400 videos for evaluation, including 5 categories of actions, and achieved an accuracy of over 98%.
Spatio-temporal interactive fusion based visual object tracking method

Visual object tracking tasks often struggle with utilizing inter-frame correlation information and handling challenges like local occlusion, deformations, and background interference. To address these issues, this paper proposes a spatio-temporal interactive fusion (STIF) based visual object tracking method. The goal is to fully utilize spatio-temporal background information, enhance feature representation for object recognition, improve tracking accuracy, adapt to object changes, and reduce model drift. The proposed method incorporates feature-enhanced networks in both temporal and spatial dimensions. It leverages spatio-temporal background information to extract salient features that contribute to improved object recognition and tracking accuracy. Additionally, the model’s adaptability to object changes is enhanced, and model drift is minimized. A spatio-temporal interactive fusion network is employed to learn a similarity metric between the memory frame and the query frame by utilizing feature enhancement. This fusion network effectively filters out stronger feature representations through the interactive fusion of information. The proposed tracking method is evaluated on four challenging public datasets. The results demonstrate that the method achieves state-of-the-art (SOTA) performance and significantly improves tracking accuracy in complex scenarios affected by local occlusion, deformations, and background interference. Finally, the method achieves a remarkable success rate of 78.8% on TrackingNet, a large-scale tracking dataset.
Authentication in Peer-to-Peer Cloud using AES and SRP

The system focuses on proposing a robust authentication framework based on these Secure Remote Password (SRP) protocol to enhance the security of data exchange in Peer-to- Peer (P2P) cloud environments. The Advanced Encryption Standard (AES) algorithm provides encryption and the SRP protocol leverages cryptographic primitives to establish mutual authentication between cloud servers and users, ensures that data, remains protected against any vulnerabilities. The system encompasses various stages, including user registration, key establishment, and secure data transmission. After user registration, the SRP protocol employs a zero-knowledge proof mechanism to maintain the security of data available on cloud servers, mitigating the risks associated with password leaks and unauthorized access. Furthermore, the protocol facilitates secure key exchange to establish a confidential communication channel between peers, enabling encrypted data transmission. The system integrates SRP protocol and AES encryption, fortifying data security in P2P cloud environments through robust authentication, ensuring a comprehensive defence against potential vulnerabilities.