Reviewerly/exHarmony huggingface dataset:
Enabling Efficient, Verifiable, and Secure Conjunctive Keyword Search in Hybrid-Storage Blockchains

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. We also design two optimized schemes to improve query and verification performance by using a partition-based method and an obfuscated counting Bloom filter mechanism. Finally, we provide a theoretical security analysis and empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed schemes.
Visualizing Historical Book Trade Data: An Iterative Design Study with Close Collaboration with Domain Experts

The circulation of historical books has always been an area of interest for historians. However, the data used to represent the journey of a book across different places and times can be difficult for domain experts to digest due to buried geographical and chronological features within text-based presentations. This situation provides an opportunity for collaboration between visualization researchers and historians. This paper describes a design study where a variant of the Nine-Stage Framework [46] was employed to develop a Visual Analytics (VA) tool called DanteExploreVis. This tool was designed to aid domain experts in exploring, explaining, and presenting book trade data from multiple perspectives. We discuss the design choices made and how each panel in the interface meets the domain requirements. We also present the results of a qualitative evaluation conducted with domain experts. The main contributions of this paper include: 1) the development of a VA tool to support domain experts in exploring, explaining, and presenting book trade data; 2) a comprehensive documentation of the iterative design, development, and evaluation process following the variant Nine-Stage Framework; 3) a summary of the insights gained and lessons learned from this design study in the context of the humanities field; and 4) reflections on how our approach could be applied in a more generalizable way.
The Effectiveness of Augmented Reality Applications in Social Studies Course

The aim of this study is to examine the effect of Augmented Reality applications on students' academic achievement and retention of learning in the learning area of "People,Places and Environments" in the "Life on Earth Unit" of the 6th Grade Social Studies Course in the 2019-2020 Fall Semester.The quasi-experimental research was conducted on a total of 62 students studying in the 6th grade of Tevfik Fikret Secondary School in Diyarbakır Sur district in the 2019-2020 Fall Semester.Of these students, 31 were assigned to the experimental group and 31 to the control group.The data were collected with an achievement test developed by the researcher.The test consisting of a total of 30 multiple-choice items was applied to both groups as pretest, posttest and retention test.The data obtained from these applications were analyzed with parametric tests such as independent groups "t" test, dependent groups "t" test and eta square after normality tests.In the study, it was concluded that teaching supported by Augmented Reality (AR) applications in middle school social studies course was more effective than the current teaching in increasing academic achievement.It was also determined that AR-supported instruction provided more permanent learning compared to the current instruction.In the study, it was evaluated that this situation may be due to the higher level of learning obtained with the cognitive and affective support provided by AR, which increases the retention of what is learned.At the end of the research, it was evaluated that the fact that AR provides both academic success and permanent learning may be due to the fact that this technology makes the messages (stimuli) more effective in the lessons and the students react to them more strongly.From this point of view, it was inferred that AR is suitable for the mental functioning of current middle school students with high virtual and digital competence.
Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

Clustering by fast search and find of density peaks (CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks (RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.
13 Virtual reality in tourism: assessing the authenticity, advantages, and disadvantages of VR tourism

The travel and tourism sectors were a major economic driver before the spread of COVID-19. COVID-19 has had a profound impact on the tourist industry, notably in the areas of marketing, sustainability, and virtual worlds, even if VR is still a relatively young development in the sector. Virtual reality (VR) is often believed to provide visitors with the “perfect” vacation. This is due to the elimination of all drawbacks experienced by visitors to traditional tourist destinations. Despite VR’s advantages and growing popularity, an important issue remains: will VR spell the death of traditional tourism, or is it only the beginning? This chapter examines the intersection between tourism and technology, with an emphasis on the part played by VR. The purpose of this chapter is to investigate whether VR tourism can replace traditional tourism or represents a distinct subset of the tourist industry. Tourism is placed and contextualized inside the VR domain, and the origins and evolution of VR are investigated in order to assess this astounding change in an ancient industry. It investigates the validity of the VR tourist experience and the pros and cons of it.
Examining Youths’ Response to Skill Acquisition as Panacea to Unemployment in Nigeria

The reactions of Nigeria’s youth to skill acquisition were examined. This was done using the Ile-Oluji/Oke-Igbo local government area as a case study. The research used both primary and secondary data. The former was through a structured questionnaire and oral interview schedule, while the latter was from the Nigeria Bureau of Statistics official website and other relevant bodies’ websites. A total of 400 respondents were randomly selected across the 10 zones of the Local Government. The results obtained showed that about 68% of the respondents were females, while 32% were males. About 34% of the respondents were self-employed with prevalent skills such as fashion, catering service, barbing and hairdressing, while 44% did not have any skills. Only 24% acquired skills for self-reliance, whereas 23% chose to pursue academic certificates ahead of skill acquisition. Peer influence was a factor which debars some of the respondents from acquiring skills. Other factors include lack of funds and epileptic power supply, which were represented by 18% and 10% of the youths, respectively. Another factor was early marriage, as about 5% got married early, and this stopped them from skill acquisition. The chi-square test carried out on the data at p-value ≤ 0.05 revealed that a significant percentage of the youths are not willing to acquire skills. Based on these premises, it is recommended that more orientation should be carried out about skills acquisition among the youths in Nigeria The contributory factors examined should also be addressed by relevant stakeholders to reduce the unemployment rate among our youths in Nigeria.
Optical character recognition with different languages

Optical character recognition is the combination of optical technology and computer technology to identify text in an image and then recognize the text content in the image, providing individuals with a great deal of ease in their daily lives. Document text recognition, natural scene text recognition, bill text recognition, and ID card recognition have been used in daily life, but there are still many factors that lead to inaccurate identification and detection. Therefore, different texts, patterns or characters are suitable for different types of Optical character recognition. In this paper, we can learn about the Optical character recognition operation methods and find the similarities and differences through researching the technical routes and four different types of Optical character recognition. In addition, by comparing the Optical character recognition of several commonly used languages, the advantages and disadvantages of each method can be analysed.
Effectiveness of principal component analysis in breast cancer diagnosis: An empirical and visualization analysis

With the development of computer science technology, it is increasingly common to apply computer science technology to the medical field, and one of the typical examples is to predict the probability of cancer cells recurrence in cancer patients. However, there is still a lack of unified explanation for the impact of the principal component analysis (PCA) dimensionality reduction method used in prediction on the final experimental results. Therefore, the research topic of this paper is the effect of PCA dimensionality reduction method on the accuracy of cancer prediction. The methodology in this paper is as follows: First, patient data were collected, based on a usable machine learning repositor. It has 569 instances, which contains both malignant and benign tumors. Second, exploratory data analysis is performed on the data, detecting outliers and discovering existing correlated data. Then, the comparative method is used to explore the effectiveness of PCA dimensionality reduction method on the prediction accuracy by observing and analyzing tables and images. It is found that the PCA dimensionality reduction method plays a positive role for boosting the prediction accuracy, but the PCA method does not significantly improve the prediction accuracy for the data that has been processed and the dimension is not high.
DenseNet-random forest model based galaxy classification

Finding an efficient and accurate adaptive method that can automatically classify galaxies has become an industry consensus. However, most of the current studies on galaxy classification use a single model for direct output, without considering the combination with other models to output more satisfactory prediction results. Through convolutional neural network and classifier, this study studied the possibility of applying the deep learning model to the Galaxy 10 DECals dataset classification, and proposed DenseNet-Random Forest model through comparative analysis. By adjusting and training DenseNet-121 with appropriate hyperparameters, the input tensor is transferred to the basic model through the creation of a shape input layer, where GlobalAveragePooling2D is added to perform an average pooling operation on each feature map, reducing the spatial dimension of each feature map to 1. During the process, a complete connection layer with 64 neurons was added using the ReLU activation function, and a Dropout layer was added to randomly discard 20% of the neurons during training to prevent overfitting. In addition, ReLU Activation function with 32 full connection layers of neurons and softmax Activation function with 10 output layers of neurons are added. By acquiring the feature vector of the training model and the real label of the verification set, assign x and y values respectively, and import them into the Random forest classifier model. The experimental results demonstrated the model ultimately achieved a prediction accuracy of 68% when processing the Galaxy 10 DECals dataset, and achieved nearly 30% improvement in Precision, Recall, and F1 scores.
A survey on federated learning: evolution, applications and challenges

Federated learning, a machine learning technique that enables collaborative model training on decentralized data, has gained significant attention in recent years due to its potential to address privacy concerns. This paper explores the evolution, applications, and challenges of federated learning. The research topic focuses on providing a comprehensive understanding of federated learning, its advantages, and limitations. The purpose of the study is to highlight the importance of federated learning in preserving data privacy and enabling collaborative model training. The study conducted a literature review by systematically analyzing relevant papers from peer-reviewed journals, conference proceedings, and reputable sources. The results reveal that federated learning offers a promising solution for collaborative machine learning while addressing concerns related to data privacy and security. The study emphasizes the need for further research in optimizing communication protocols, scalability, and privacy-preserving techniques. Overall, this paper contributes to the understanding of federated learning and its potential for secure and efficient decentralized learning paradigms.
An overview of research results and applications in the field of semi-supervised semantic segmentation

Traditional segmentation approaches, supervised deep learning methods, and semi-supervised deep learning methods have all found widespread use as the field of semi-supervised semantic segmentation has advanced. These methods have developed and progressed over time, opening up novel avenues of research in the field of image segmentation and giving potent resources for tackling difficult practical issues. These developments have deepened our understanding of image segmentation and provided flexible and efficient solutions to challenges in practical applications, ranging from classical traditional approaches to supervised methods based on deep learning, and beyond to semi-supervised methods that leverage both labeled and unlabeled data. Focusing on their specialized applications in medical and remote sensing image processing, this paper presents a complete overview of the development status of these methods. This study's image segmentation solutions can help tackle actual-world issues where annotated data is rare or expensive to some extent.
A Comparison of LSTM and BERT model for sarcasm prediction

Sarcasm prediction is a text analysis task that aims to identify sarcastic and non-sarcastic statements in text. Sarcasm is a figure of speech that uses opposite or contradictory language to express a certain meaning or idea. Sarcasm is usually cryptic, vague, and suggestive, which makes sarcasm prediction a challenging task. In sarcasm prediction projects, techniques of natural language processing are usually leveraged to analyze and classify the text. The main challenge of this task lies in the fact that sarcasm usually has multiple manifestations and needs to consider the contextual and semantic information of the text. The prediction of sarcasm holds significant application value in natural language processing, such as social media analysis, public opinion monitoring, sentiment analysis and so on. In this paper, by controlling variables, the influence of adding the long short-term memory (LSTM) layer and changing the grid structure of the model on the accuracy of prediction results is explored. Moreover, accuracy of the LSTM prediction performance is compared with that of the bidirectional encoder representations from Transformers (BERT) model. At the same time, this paper analyzed and discussed the phenomenon that adding the number of LSTM model layers could not obtain higher prediction accuracy, and the accuracy gap of prediction results between LSTM model and BERT model, and finally obtained relevant conclusions.
A research on the consensus mechanisms

A distributed and decentralized ledger widely used in the computer science and financial fields called blockchain has provided safe and fast transactions for multiple parties. Also, check the transaction by each node on the blockchain. The consensus mechanism is the core of the blockchain. It lets all the nodes reach an agreement for those transactions, which ensures security and accuracy and make Bitcoin valuable and popular. Two of the most mainstream Consensus mechanisms are Proof of Work (PoW) and Proof of Stake (PoS), and Proof of Authority (PoA) is the new one that will apply in the future. Many discourses talk about consensus mechanisms, most of which are review papers. Those papers mainly show a specific aspect of a consensus mechanism or introduce the primary notion, but they rarely explain the corresponding relationship between theories and cryptocurrency. So, the purpose is to give a clear structure, connect the consensus mechanism to its application and simplify the reader's understanding. This paper aims to provide an overview of the consensus mechanism, including its general definition, concepts of different mechanism variants, and advantages and disadvantages. For the structure below, the essay introduces the notion of consensus mechanism and how PoW, PoS, and PoA work. Then summarize the papers based on these three consensus mechanisms, describing the theories of many consensus mechanisms and comparing the advantages and disadvantages. The essay also creates a comparison table about these three consensus mechanisms to embody the content above the stem better.
Revolutionizing student course selection: Exploring the application prospects and challenges of blockchain token voting technology

This paper explores the utilization of blockchain token voting technology in student course selection systems. The current course selection systems face various issues, which can be mitigated through the implementation of blockchain technology. The advantages of blockchain technology, including consensus mechanisms and smart contracts, are discussed in detail. The token voting mechanism, encompassing concepts, token issuance and distribution, and voting rules and procedures, is also explained. The system design takes into account the system architecture, user roles and permissions, course information on the blockchain, student course selection voting process, and course selection result statistics and public display. The technology offers advantages such as transparency, fairness, data security and privacy protection, and system efficiency improvement. However, it also poses several challenges, such as technological and regulatory hurdles. The prospects for the application of blockchain token voting technology in student course selection systems and its potential impact on other fields are summarized. Overall, the utilization of blockchain token voting technology in student course selection systems holds promising future implications, which could revolutionize the education sector.
Research on the application of machine learning models in speech recognition in noisy environments

In a world increasingly reliant on verbal communication with machines, this paper undertakes a rigorous exploration of the application of machine learning models in speech recognition within noisy environments. An important focus of this research lies in demonstrating the viability and robustness of these models when challenged by real-world noise interference. In particular, the utilization of deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is thoroughly examined. Using concrete data and case studies, this paper uncovers a promising trajectory in the development and implementation of noise-robust speech recognition systems. In so doing, it lays a foundation for future research to enhance the practicality and effectiveness of machine learning models in this area. The ultimate goal is to ensure more reliable and accurate speech recognition that can withstand various noise conditions, thus expanding the potential for speech recognition applications in diverse and adverse environments.
Multi-scale filter-enhanced transformer for sequential recommendation

Recent research has shown that self-attention mechanisms improve Sequential Recommender Systems (SRS) by capturing sequential associations with the interactions. Nevertheless, existing work still needs to address two critical limitations. Firstly, the behavior of users in the original sequences contains various preference signals that are implicit and noisy and hard to reflect the users intentions fully. As a result, it would deteriorate the representation of their true intentions to model all interactions. Secondly, most models only model single-scale interaction sequences and ignore the multi-scale feature relationships of the sequences. In order to address these limitations, the paper proposes MFTSRec (Multi-scale Filter Enhanced Transformer Sequential Recommender), which can weaken those interactions irrelevant to the users intentions from their implicit feedback and adaptively focus on the users multi-scale intentions. Besides, this paper also does extensive experiments on four benchmark datasets and further demonstrates the effectiveness and robustness of MFTSRec compared to the state-of-the-art model.
Fast Encryption Scheme for Secure Transmission of e-Healthcare Images

E-healthcare systems (EHSD), medical communications, digital imaging (DICOM) things have gained popularity over the past decade as they have become the top contenders for interoperability and adoption as a global standard for transmitting and communicating medical data. Security is a growing issue as EHSD and DICOM have grown more usable on any-to-any devices. The goal of this research is to create a privacy-preserving encryption technique for EHSD rapid communication with minimal storage. A new 2D logistic-sine chaotic map (2DLSCM) is used to design the proposed encryption method, which has been developed specifically for peer-to-peer communications via unique keys. Through the 3D Lorenz map which feeds the initial values to it, the 2DLSCM is able to provide a unique keyspace of 2544 bits (2^544bits) in each go of peer-to-peer paired transmission. Permutation-diffusion design is used in the encryption process, and 2DLSCM with 3DLorenz system are used to generate unique initial values for the keys. Without interfering with real-time medical transmission, the approach can quickly encrypt any EHSD image and DICOM objects. To assess the method, five distinct EHSD images of different kinds, sizes, and quality are selected. The findings indicate strong protection, speed, and scalability when compared to existing similar methods in literature.