Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Framework for Smart Tourism

The rapid development and globalization of modern tourism have brought more and more tourists and tourist attractions, bringing opportunities to the tourism industry. However, challenges also come with it, including the cost of travelers’ trust, the satisfaction of personalized tourism needs, and the supervision and management of tourist attractions. In order to solve these problems and improve the tourism experience, this paper designs a blockchain-based smart tourism platform. First, by adopting blockchain technology, a decentralized database is established to reduce the cost of trust for travelers. Secondly, through the blockchain-based smart tourism platform, travelers can customize personalized travel routes and itineraries according to their preferences and needs. The platform uses the data interaction function of blockchain to visually display various resources and services of tourist attractions, and travelers can choose according to their own preferences to enhance the personalized experience of tourism. Third, the smart tourism platform based on the blockchain can provide the reliability and transparency of data information, and through the decentralized nature of blockchain, the relevant data of tourist attractions can be recorded and stored, and cannot be tampered with, ensuring the authenticity and credibility of the data. Regulatory authorities can easily access and review these data, and more effectively control and supervise scenic spots. Finally, this paper combines DPOS (Delegated Proof of Stake) and PBFT (Practical Byzantine Fault Tolerance) consensus algorithms to improve the on-chain consensus efficiency of blockchain-based smart tourism platforms. The consensus algorithm is the core mechanism to ensure the normal operation and consistency of the blockchain system, and improve the throughput and performance of the system by introducing DPOS and PBFT algorithms.
Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region

Clouds have a significant impact on the planet's energy balance, climate, and weather. They serve as the primary temperature regulator and function as a blanket to absorb thermal energy or longwave radiation. The present study estimates the percentage of rainfall clouds within a 100-kilometer radius of Fujairah City on the Gulf of Oman using image processing based on machine learning and digital image processing. The data for 9 months starting from January 2022 to October 2022 has been retrieved from the Copernicus satellite data component through the Sentinel 3 LST F2 channel. K-mean cluster analysis has been used to validate the accuracy of an algorithm which is applied to determine cloud cover, with a precision rate of 99.9% for clear weather and 95.5% for overcast conditions. The findings indicate that most of the rainy clouds were observed during the months of January and July. The remaining duration of the year exhibits a reduced occurrence of these clouds. Beginning in February, the region of interest experiences cloud cover accompanied by precipitation subsequent to the month of January. Similarly, the month of July exhibited cloud covers with moisture. Throughout the year, dry clouds are observed with moderate coverage percentages. However, there are no observations of any of these clouds during the months of May and December. In summary, automated systems for observing clouds in the atmosphere are a valuable method for detecting cloud cover and predicting climatic patterns in diverse geographical locations.
Evaluation of Risk Factors for Falls Among Older Adults Living Alone Using Machine Learning

Abstract Date Presented 04/21/2023 Some physical and mental health variables were identified as important factors of falls among older adults. This study investigated the risk factors that are associated with falls among older adults living alone using machine learning algorithms. Primary Author and Speaker: Suyeong Bae Contributing Authors: Ickpyo Hong, Daewoo Pak
E-Commerce and Cyber Vulnerabilities in Bangladesh

The e-commerce growth scenario is forging a new dynamism in business and does not offer time-bound, cost-effective and hassle-free buying and selling. Following the changing approach, security management in e-commerce (cyber security) is now a focus. This research is a qualitative research that uses a case study method to analyze risks and vulnerabilities in the field. People in Bangladesh are primarily unaware of the risks related to using computing and digital devices and online platforms. This article articulates the risk factors associated with the growing reliance on digital technologies and devices. Then, some policy guidelines are prescribed for the national level and individual level, including leading social campaigns, new chapters in the school curriculum, advancing technological aptitudes of law enforcers, strengthening security systems in e-commerce, embanking sites, bringing frauds to justice, initiating law to define cybercrimes, fortifying institutional management of cyber securities.
A Programmable Qudit-based Quantum Processor

We designed, fabricated, and characterized a programmable qudit-based quantum processor on silicon. We implemented several quantum algorithms with qudits, showing the logarithmic speed-up, photon detection rate enhancement and accuracy improvement in the qudit-based quantum information processing.
A novel privacy protection method of residents’ travel trajectories based on federated blockchain and InterPlanetary file systems in smart cities

The government does have to record and analyze the travel trajectories of urban residents aiming to effectively control the epidemic during COVID-19. However, these privacy-related data are usually stored in centralized cloud databases, which are prone to be vulnerable to cyber attacks leading to personal trajectory information leakage. In this article, we proposed a novel secure sharing and storing method of personal travel trajectory data based on BC and InterPlanetary File System (IPFS). We adopt the Hyperledger Fabric, the representative of Federated BC framework, combined with the IPFS storage to form a novel mode of querying on-chain and storing off-chain aiming to both achieve the effectiveness of data processing and protect personal privacy-related information. This method firstly solves the efficiency problem of traditional public BC and ensures the security of stored data by storing the ciphertext of complete personal travel trajectory data in decentralized IPFS storage. Secondly, considering the huge amount of information of residents’ travel trajectories, the method proposed in this article can obtain the complete information under the chain stored in IPFS by querying the index on the chain, which significantly improves the data processing efficiency of residents’ travel trajectories and thus promotes the effective control of the new crown pneumonia epidemic. Finally, the feasibility of the proposed solution is verified through performance evaluation and security analysis.
High-Rate Quantum Key Distribution Exploiting Integrated Photonics and Multi-Pixel Superconducting Nanowire Detectors

We report a quantum key distribution system that is able to generate key at a record high key rate of 115.8 Mb/s over 10-km fibre. This attributes to a high-efficiency multi-pixel superconducting nanowire detector, a low-error integrated transmitter, and a fast post-processing algorithm.
Temperamental characteristics of pre-teens at risk of early school leaving

School dropout represents a serious problem, being seen as a violation of moral norms. The aims of the investigation were to identify the temperament of pre-teens at risk of school dropout, and also to emphasize differences in temperament considering type
Piezoelectric Programmable Optical Mode Conversion in a Photonic Integrated Circuit

Waveguide-based optical mode conversion requires wavelength-scale patterning of the waveguide's optical properties. We implement a programmable version of this patterning with piezoelectrically actuated photonics.
Photonic frequency synthetic dimensions for high-dimensional convolutions

We show that high-dimensional convolution can be realized in a single dynamically modulated optical ring resonator. The high-dimensional convolution introduced here has broad applications, such as 3D convolution for motion detection and scene reconstruction.
Teaching and Learning Process of English Speaking Skills at SMKN 27 Jakarta Pusat Majoring in Tourism – A Case Study

Students of tourism vocational schools require mastering speaking skills in English to be able to handle guests from various backgrounds. Yet, the preliminary research showed that 60% of students in SMKN 27 Jakarta Pusat could not speak English fluently. This study aimed at describing the design, teachers' plan, teaching methods, materials, teachers' role, and students' role, and assessment techniques of English speaking teaching and learning. This study used a case study with observation and interview as the research technique. The findings of this study showed that the syllabus design used in SMKN 27 was a functional-based syllabus that was suitable for the vocational high school students. The teaching methods used in teaching and learning were the Direct Method and Audiolingual Method. The materials used by the teachers in teaching and learning process were videos and an English coursebook in most of the observations' meetings. Teachers acted as a prompter and a feedback provider, helped the students when they found any difficulties in expressing themselves using the target language and gave feedback directly. The assessment techniques used were summative and formative assessment and the teachers used mid-term test and final-term test as the assessment techniques. &#x0D; &#x0D;
Experimental symmetric private information retrieval with measurement-device-independent quantum network

We report the first realisation of a provably-secure symmetric private information retrieval (SPIR) scheme supported by a quantum-secure key-exchange network, offering secure retrieval of fingerprint data from a database with 800 entries.
Deep Learning-Based Methods for Multi-aspect Data Clustering

Deep learning-based clustering approaches, which utilize deep neural networks to learn latent feature representations for data clustering, have garnered significant attention in the fields of machine learning and data mining. In the context of multi-aspect data, these methods focus on designing various network architectures, loss functions, and fusion techniques to leverage the power of deep learning. Inspired by the success of deep learning in artificial neural networks, matrix factorization-based methods have also been extended to incorporate deep hierarchical learning. This involves progressively projecting data from higher to lower-order representations through multiple layers. This chapter provides a comprehensive review of two classes of deep learning methods for clustering multi-aspect data. The first class is based on artificial neural network architectures and learning algorithms, while the second class is based on deep matrix factorization algorithms. The chapter presents a taxonomy and in-depth discussion of these techniques, including the Autoencoder approach, Generative Adversarial Network approach, and Deep Matrix Factorization approach. It serves as a valuable resource for understanding and applying deep learning in the context of multi-aspect data clustering.
Parsing Semi-structured Languages: A Crochet Pattern to Diagram Translation

Parsers for semi-structured natural languages must be able to parse both structured and free text. The common approach for these parsers is to make use of natural language processing to parse the free text sections. This paper investigates the semi-structured natural language found in crochet patterns. We show that the natural language found in crochet patterns is restrictive enough for typical multi-phase methods used in compilers to parse these patterns. To measure the success of the pattern parsing, we translate patterns to crochet diagrams. Two different parsers are implemented. The first parser creates a uniform, structured representation of the crochet pattern. We define a formal language for this representation, that can be applied to most crochet patterns. The second parser translates the formal language to a crochet diagram. This approach proves to be successful for a wide range of different patterns.
Application of Collaborative- Interactive Methods in Teaching Literature (Reading) in Primary School

The main purpose of this article is to present the definition of the author's term “collaborative-interactive teaching methods”. In addition to deriving this definition, six collaborative-interactive methods will be presented in a practicalapplied plan. This would give the primary teachers the opportunity to apply them when teaching the “Literature Education” section of the complex school subject Bulgarian Language and Literature. The specific thing about the methods is that they are unpopular and innovative for the Bulgarian primary school. The main methods that were used in that scientific research are theoretical analysis and theoretical synthesis of over 250 methods, from which six were selected and modified.
Character Processing

This chapter introduces some of the ready-to-use functions to work with characters. Studied functions are defined in the ctype.h header file.
An Unsupervised Domain Adaptive Network Based on Category Prototype Alignment for Medical Image Segmentation

Computer-aided diagnosis is driven by data, the difference in the distribution of training and testing data leads to significant degradation in semantic segmentation model performance, known as the domain shift phenomenon. The unsupervised domain adaption method has become one of the solutions. Unsupervised domain adaption based on feature alignment is the mainstream method to reinforce feature alignment by reducing the variance of domain invariant features within a batch. However, when processing unsupervised domain adaption segmentation tasks of medical images, the feature alignment-based methods cannot learn domain invariant features stably due to the characteristics of a considerable distribution distance between features in the same category in medical images, resulting in degraded segmentation performance. To address this problem, we propose the Category Prototype Alignment Network(CPAN). First, we refine the category representative features from the domain invariant features. Second, unlike previous feature alignment methods, we construct category prototypes updated by multiple representative features to represent category features, and perform category alignment by reducing the distribution differences of category prototypes across domains, thus stabilizing feature alignment and enabling the network to learn category representative features. Experimental results of cross-domain thyroid ultrasound image segmentation show that our proposed method achieves comparable or even higher performance than current state-of-the-art methods.
3D Path Planning Based on Improved Teaching and Learning Optimization Algorithm

An improved teaching and learning algorithm suitable for global path planning is proposed for the 3D path planning problem of unmanned aerial vehicles. In order to improve the shortcomings of basic teaching and learning algorithms in practical applications, group teaching is implemented in the teaching stage to improve the local development and optimization ability of algorithms; Add autonomous learning after the learning stage to improve the algorithm's global optimization ability. The simulation results show that compared with the teaching and learning algorithm, artificial fish swarm algorithm, and ant colony algorithm, using the improved teaching and learning algorithm for unmanned aerial vehicle 3D path planning is more effective and cost-effective.
Collaborative Face Privacy Protection Method Based on Adversarial Examples in Social Networks

The face image de-identification method commonly used to protect personal privacy on social networks faces the problem of not being able to assure the usability of image sharing. Although the privacy protection method based on adversarial examples meets this requirement, there are still potential hidden problems of information leakage and the compression processing of images by social networks will weaken the privacy protection effect. In this paper, we propose a collaborative privacy protection method based on adversarial examples for photo sharing services on social networks, called CSP3Adv(Collaborative Social Platform Privacy Protection Adversarial Example). We use the perturbation transfer module, which avoids the information leakage caused by accessing to the original image. Moreover, we use the frequency restriction module to guarantees the privacy of users' face images after social network compression. The experimental results show that CSP3Adv achieves better privacy protection for various face recognition models and commercial API interfaces on different datasets.
SA-GAN: Chinese Character Style Transfer Based on Skeleton and Attention Model

The Chinese character transfer task must meet two requirements: the transfer image should retain the content structure information of the original Chinese character as much as possible, and present different reference styles. Some of the earlier methods required training with large amounts of paired data, which was a time-consuming task. The existing method follows the normal form of style-content disentanglement, and realizes style transfer by combining the reference Chinese character style. This method is easy to cause the problem of missing stroke content and inaccurate overall style transfer. To address these issues, a generation network based on the Chinese character skeleton and attention model is proposed. To further ensure the completeness of the content of the converted Chinese characters, a more efficient upsampling module is introduced to improve the quality of the converted Chinese characters. Through extensive experiments, it is shown that the model requires only one reference Chinese character to produce higher quality Chinese character images than the current state-of-the-art methods.
Enhancing Healthcare Model Trustworthiness Through Theoretically Guaranteed One-Hidden-Layer CNN Purification

The use of Convolutional Neural Networks (CNNs) has brought significant benefits to the healthcare industry, enabling the successful execution of challenging tasks such as disease diagnosis and drug discovery. However, CNNs are vulnerable to various types of noise and attacks, including transmission noise, noisy mediums, truncated operations, and intentional poisoning attacks. To address these challenges, this paper proposes a robust recovery method that removes noise from potentially contaminated CNNs and offers an exact recovery guarantee for one-hidden-layer non-overlapping CNNs with the rectified linear unit (ReLU) activation function. The proposed method can recover both the weights and biases of the CNNs precisely, given some mild assumptions and an overparameterization setting. Our experimental results on synthetic data and the Wisconsin Diagnostic Breast Cancer (WDBC) dataset validate the efficacy of the proposed method. Additionally, we extend the method to eliminate poisoning attacks and demonstrate that it can be used as a defense strategy against malicious model poisoning.
Undetectable Attack to Deep Neural Networks Without Using Model Parameters

Deep neural networks (DNNs) have been widely deployed in a diverse array of tasks, such as image classification. However, recent research has revealed that intentionally adding some perturbations to the input samples of a DNN can cause the model to misclassify the samples. The adversarial samples have the capability of fooling highly proficient convolutional neural network classifiers in deep learning. The presence of such vulnerable ability in these neural networks may have severe implications on the security of targeted applications. In this work, we show that attacks on CNNs can be successfully implemented even without knowing model parameters of the target network. We use the beetle antennae search algorithm to realize the attack such that human eyes cannot detect the attack. Compared to other adversarial attack algorithms, the resulting adversarial samples from our algorithm are not significantly different from the pre-attack images, which makes the attack undetectable. In this study, the CIFAR-10 dataset was utilized to show the efficacy and advantages of the algorithm on LeNet-5 and ResNet architectures. Our findings indicate that the proposed algorithm produces images with no significant difference from the original images while the attack success rate is high.
A Weakly Supervised Semantic Segmentation Method on Lung Adenocarcinoma Histopathology Images

Accurately distinguishing different tissues is crucial for pathologists to determine the type and degree of lesions. The methods based on class activation maps (CAM) are popular in current research due to their low annotation cost. However, this method has two main drawbacks: (1) as the training deepens, the class activation maps become increasingly concentrated in the most discriminative region and cannot fully cover the object; (2) different tissues may be activated at the same pixel because the binary cross-entropy (BCE) calculates classification loss independently. To alleviate these issues, we propose a new method that combines channel attention (CA) and progressive dropout attention (PDA) to expand the class activation maps and enhance the activation of important features. In addition, after the network converges, we train an additional fully-connected layer and use softmax cross-entropy (SCE) to fine-tune the model parameters and reduce the activation of unrelated tissues. We evaluate our proposed model on the recently released LUAD-HistoSeg dataset. The results illustrate that our model achieved a 76.53% MIoU and outperform the most advanced methods.
IL-YOLOv5: A Ship Detection Method Based on Incremental Learning

Traditional target detection algorithms based on deep learning require a large number of ship sample data sets to be trained to achieve better detection performance. However, obtaining a large number of data samples is difficult and may suffer from the problem of catastrophic forgetting. To address these issues, this paper proposes an incremental learning based ship detection method called IL-YOLOv5 (Incremental Learning YOLOv5). IL-YOLOv5 employs an improved BiFPN and a coordinate attention mechanism to improve its ability to extract ship-related features. Next, a base dataset of ship classes is trained to create a standard ship detection model, and finally, the incremental learning method is used to continuously modify the model and learn the characteristics of new ship samples. The experimental results show that the model has a good detection performance on both old and new ship datasets. The accuracy rate has reached nearly 72%, and the @mAp.5 has increased by 5.3%, effectively addressing the difficulty of collecting a large number of datasets and catastrophic forgetting.