Reviewerly/exHarmony huggingface dataset:
SVM-ANN Optimized Algorithm for the Classification of Breast Cancer Data as Benign and Malignant

The second most widespread cancer in women, breast cancer, is the most lethal and is responsible for an increasing number of daily deaths in females. Breast cancer patients who receive an advanced diagnosis have a higher probability of dying and reduces the probability of surviving. Numerous research projects are being implemented in order to improve breast cancer rapid identification. Despite the existence of numerous medical diagnostic techniques for predicting breast cancer, it remains difficult to anticipate breast cancer at its earliest stages. The major objective of this research is to establish a predictive model that could really identify breast cancer earlier on and increase survival rates. The Wisconsin Breast Cancer (Original) Dataset is utilized in this research to forecast breast cancer utilizing techniques from data mining like K-Nearest Neighbor Algorithm(KNN), Support Vector Machine Algorithm(SVM), and Artificial Neural Network Algorithm(ANN), a new model SVM-ANN optimized algorithm is also proposed. A variety of parameters which including Accuracy, Precision, and Recall, have been employed to compare the results of these algorithms in order to fully evaluate their effectiveness. Finally, with a 97 percent accuracy rate, the proposed algorithm SVM-ANN optimized algorithm significantly outperformed other current algorithms.
Main-Secondary Blockchain Framework: Cross-Domain Trust Management Mechanism Using Trust Ticket

Industrial Internet of Things (IIoT) can facilitate smart manufacturing and increase productivity through revolutionary techniques. As production processes are getting more sophisticated, an entire production process is usually completed collaboratively by multiple administrative domains (e.g., factories). Trust management is an effective solution to establish trust relationship between the devices from different administrative domains. Recently, many researchers integrate blockchain into trust management mechanisms. However, the existing methods have the shortcoming of low throughput, high delay and storage overhead, so cannot meet scalability requirements of large-scale IIoT multi-domains scenarios. Therefore, this paper proposes a main-secondary chain framework to realize a scalable cross-domain trust management mechanism (MSB-CTrust), where trust tickets are used to resolve the heterogeneity issue of trust evaluation methods in different domains. Besides, MSB-CTrust provides embedded salary distribution rules, which are used to encourage all devices to behave honestly based on a revenue strategy game. Performance evaluations in the experiments show the feasibility and effectiveness of MSB-CTrust.
Incentive Mechanism Design for Mitigating Frontrunning and Transaction Reordering in Decentralized Exchanges

Decentralized exchanges (DEXes) provide effective price discovery and fair trading while dealing with the drawbacks of centralized exchanges, e.g., lack of transaction transparency and exclusive control of user assets and transaction fees.However, many DEXes suffer from frontrunning and transaction reordering, which fundamentally flaw their design.In this paper, we present a novel incentive mechanism design for mitigating frontrunning and transaction reordering even if frontrunners pay high transaction fees in DEXes.We utilize a weighted counting sort algorithm to order transactions based on the users' multi-dimensional private information (e.g., transaction delay and confidentiality).To elicit users' private information, we consider a multi-dimensional contract-theoretic design based on the users' willingness to share their private information.We show that the miner can always maximize its utility under the complete and incomplete information scenarios.We implement solutions to our multi-dimensional contract and sorting algorithm on a decentralized oracle network to create a decentralized system and design a web application to extensively evaluate the performance of our proposed incentive mechanism.We further show that ordering transactions based on users' private information increases the miner's utility by 78.42% -84.57% and reduces the users' cost by 64.47% compared with the state-of-the-art fair sequencing services, automated arbitrage market maker, and miner extractable value auctions.
UAV Image Haze Removal Based on Saliency- Guided Parallel Learning Mechanism

Current haze removal methods for unmanned aerial vehicle (UAV) images are mostly based on natural image dehazing methods, which ignore the particular imaging mechanism of UAVs. They often fail to restore regions with rich spectral and textural information. In this letter, we propose a saliency-guided parallel learning mechanism for UAV image haze removal. First, we design a saliency-guided parallel dehazing module with two parallel paths. The residual feature extraction path obtains deep-level features to realize global dehazing effectively. The key feature enhancement path, which comprises saliency dense blocks, realizes local textural preservation and spectral restoration. Second, a sporadic foreground saliency detection method is proposed for UAV images with sporadic objects. The saliency map guides the learning of significant spectral and textural information in hazy images. Finally, a multiscale reconstruction module is introduced to more accurately estimate small-scale textural details and large-scale spectral information. Experimental results show that the proposed method has better detail performance and visual effects than state-of-the-art methods.
Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks

We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. The qubit-resonator couplings are assumed to be in the resonant regime and tunable in time. A genetic algorithm is used in order to find the functional time-dependence of the couplings that optimise the fidelity between the evolved state and a variety of targets, including three-qubit GHZ and Dicke states and four-qubit graph states. We observe high quantum fidelities (above 0.96 in the worst case setting of a system of effective dimension 96) and resilience to noise, despite the algorithm being trained in the ideal noise-free setting. These results show that the genetic algorithms represent an effective approach to control quantum systems of large dimensions.
Upper-air meteorological dataset for Uyo, using radiosonde

Weather pattern anomalies and climate change have greatly impacted human activities and the environment in varying ways. Whether induced naturally or by anthropogenic activities, it remains a menace to global public health. A foreknowledge of the weather/climate change can help in mitigating the impact of disasters emanating from these changes. Upper-air meteorological data play an exceptionally large role in weather and climate prediction. However, there is a paucity of ground truth meteorological data in Nigeria and many parts of Africa. Consequently, the need to measure and archive these data. Internet of things and blockchain technologies are employed to build a system that captures and records meteorological data at up to 9,000 metres above sea level. Spanning between January 18, 2021 and July 26, 2021, in Uyo local government area, upper air pressure, temperature, dew point, time and the elevation at which they were captured, are the meteorological data presented in this data article.
Artificial Intelligence Applications in Literary Works

The British Pakistani writer, Mohsin Hamid's debut novel, Moth Smoke (2000), has garnered conflicting responses from readers across the globe. Over the past few years and with the rapid advancements in social media platforms, readers around the world have publicly shared their opinions and feelings towards the text using online platforms such as Twitter, Goodreads, Facebook—among many others. The huge bulk of readers' reviews are useful data for publishers and booksellers in analyzing readers' interests to recommend similar texts to online readers. The analysis of sentiment and emotion attached to this data can help to determine the popularity or unpopularity of a literary text. Using reader-reviews of Hamid's novel from Goodreads as the main data source, this study a offers a data analytic approach: LSTM, LDA to detect and classify the dominant emotion existing within the readers' feedback. Understanding readers' emotions towards the novel can help in developing a recommendation system that can suggest readers stories of their interest.
Detecting corporate financial fraud via two-stage mapping in joint temporal and financial feature domain

Corporate financial fraud (CFF) has great harmful influences on firms, stakeholders, and capital markets. For CFF detection, firm-year or firm-quarter data are frequently utilized to construct financial feature variables and they have been proved to be effective. However, the firm-year or firm-quarter data are sampled at a single time step only, and the information in the temporal domain has not been fully exploited. As such, we model the corporate financial data into a three-dimensional (3-D) data cube that incorporates both temporal and financial feature domain information. This information can be jointly processed by a two-stage mapping scheme. The first stage maps the joint domain data into discrete sparsity patterns, and the second stage learn some high-level low-dimensional feature representation from the first stage output. The proposed two-stage mapping scheme is tested with real financial data obtained from China Stock Market and Accounting Research (CSMAR) database, and is found to work well. We find that the detection performance is dramatically improved when the accumulated number of quarters increased, and a 3-year, i.e., 12-quarter processing interval will be enough for CFF detection. This finding is important because it is intuitive and similar joint processing interval can be found on other data set. We also find out that joint temporal and financial feature domain processing is helpful for CFF detection. Furthermore, the proposed two-stage mapping scheme for joint processing in temporal and financial feature domain is intuitive for developing intelligent CFF detection systems.
An enhanced attentive implicit relation embedding for social recommendation

Nowadays, recommendation system incorporating social networks has attracted a lot of research attention and is widely used to understand user preferences regarding social relations. Besides, graph neural networks(GNNs) have proven to learn graph-structured data effectively. Thus, integrating GNN into social recommendations has become one challenge. Many approaches encode both the user–item interaction and social relations to learn user preference. However, implicit relations are also crucial to understanding the features of users and items. Thus, we propose the attentive implicit relation embedding for social recommendation(SR-AIR), which models the user–item interaction and social networks, utilizing a graph attention mechanism on implicit relations of users and items. We evaluate our framework on two real-world datasets and demonstrate that our framework performs the best compared with state-of-the-art baselines.
A novel method of data and feature enhancement for few-shot image classification

Deep learning has shown remarkable performance in quantity of vision tasks. However, its large network generally requires quantity of samples to support sufficient parameters learning during training process. Such high request greatly reduces efficiency when applying on a small dataset with few samples. To alleviate this problem, we propose a novel data enhancement method for few-shot learning via a cutout approach and feature enhancement. After enhancement, the generated network not only produces distinguish feature map without collecting more samples, but also achieves advantage of feature representation with high efficiency for computing. Specifically, cutout approach is simple yet highly effective for image regulation, which enhances input image matrix by adding a fixed mask to improve robustness and overall performance of network. Afterward, we perform feature enhancement by proposing a feature promotion module, which uses characteristics of dilated convolution and sequential processing to improve feature representation ability, thus improving efficiency of the whole network. We conduct comparative experiments on both miniImageNet and CUB datasets, where the proposed method is superior to comparative methods in both 1-shot and 5-shot cases.
Efficient SCAN and Chaotic Map Encryption System for Securing E-Healthcare Images

The largest source of information in healthcare during the present epidemic is radiological imaging, which is also one of the most difficult sources to interpret. Clinicians today are forced to rely heavily on therapeutic image analysis that has been filtered and sometimes performed by worn-out radiologists. Transmission of these medical data increases in frequency due to patient overflow, and protecting confidentiality, along with integrity and availability, emerges as one of the most crucial components of security. Medical images generally contain sensitive information about patients and are therefore vulnerable to various security threats during transmission over public networks. These images must be protected before being transmitted over this network to the public. In this paper, an efficient SCAN and chaotic-map-based image encryption model is proposed. This paper describes pixel value and pixel position manipulation based on SCAN and chaotic theory. The SCAN method involves translating an image’s pixel value to a different pixel value and rearranging pixels in a predetermined order. A chaotic map is used to shift the positions of the pixels within the block. Decryption follows the reverse process of encryption. The effectiveness of the suggested strategy is evaluated by computing the histogram chi-square test, MSE, PSNR, NPCR, UACI, SSIM, and UQI. The efficiency of the suggested strategy is demonstrated by comparison analysis. The results of analysis and testing show that the proposed program can achieve the concept of partial encryption. In addition, simulation experiments demonstrate that our approach has both a faster encryption speed and higher security when compared to existing techniques.
Eyes gaze detection based on multiprocess of ratio parameters for smart wheelchair menu selection in different screen size

Smart wheelchairs support paralysis disability people, especially paralysis of the hands and feet. Paralysis disability cannot operate menu selection with a touch screen. This research proposes a menu selection to select features in a smart wheelchair using the eye’s gaze and execute the menu by blinking the left eye. Multiprocess of ratio parameter is the methodology used and expanded in this research. The proposed method compared the ratio between the midpoint on the iris contour and registered midpoint on the selection step. This research is conducted under two conditions: the screen size being started in 14 and 17 inches. This research resulted in 91.48% and 90.68% accuracy in 14 and 17-inch screens for users without glasses. Meanwhile, 89.55% and 86.99% were obtained for wearing glasses users. The experiments were conducted where the position of a user is 30–39, 40–49, and 50–59 cm from the camera.
Toward a Comprehensive List of Necessary Abilities for Human Intelligence, Part 1: Constructing Knowledge

In [1], Adams et al. chart a roadmap toward the grand AI vision, with human-level (or greater) intelligence as destination. To that end, in this and a companion paper [2], I take one of the next steps they outline, to “refine the list of specific competency areas” in human cognition. It is argued that we should move toward a comprehensive list of all required abilities to make clearer what is known, unknown, and what the next steps should be, such as resolving how abilities piece together into the larger-scale puzzle of general intelligence. This paper concentrates roughly on the first half of cognitive processing, from initial input to knowledge construction and memory storage (including, for example, emotion, perception, attention, memory, and knowledge construction processes, such as reasoning, imagination, and simulation); with the second paper on the action-based second half that uses the knowledge for constructive outcomes.
Dynamic and Evolving Neural Network for Event Discrimination

Artificial general intelligence (AGI) should be founded on a suitable framework, e.g. a rule-based design or Deep Learning (DL). Here we choose the DL to be the basis for AGI. An appropriate AGI is defined, followed by its appropriate DL implementation. We introduce an AGI, in the form of cognitive architecture, which is based on Global Workspace Theory (GWT). It consists of a supervisor, a working memory, specialized memory units, and processing units. Additional discussion about the uniqueness of the visual and the auditory sensory channels is conducted. Next, we introduce our DL module, which is dynamic, flexible, and evolving or growing. It can be also considered as a Network Architecture Search (NAS) method. It is a spatial-temporal model, with a hierarchy of both features and tasks, tasks such as objects or events.
Object-Aware 3D Scene Reconstruction from Single 2D Images of Indoor Scenes

Recent studies have shown that deep learning achieves excellent performance in reconstructing 3D scenes from multiview images or videos. However, these reconstructions do not provide the identities of objects, and object identification is necessary for a scene to be functional in virtual reality or interactive applications. The objects in a scene reconstructed as one mesh are treated as a single object, rather than individual entities that can be interacted with or manipulated. Reconstructing an object-aware 3D scene from a single 2D image is challenging because the image conversion process from a 3D scene to a 2D image is irreversible, and the projection from 3D to 2D reduces a dimension. To alleviate the effects of dimension reduction, we proposed a module to generate depth features that can aid the 3D pose estimation of objects. Additionally, we developed a novel approach to mesh reconstruction that combines two decoders that estimate 3D shapes with different shape representations. By leveraging the principles of multitask learning, our approach demonstrated superior performance in generating complete meshes compared to methods relying solely on implicit representation-based mesh reconstruction networks (e.g., local deep implicit functions), as well as producing more accurate shapes compared to previous approaches for mesh reconstruction from single images (e.g., topology modification networks). The proposed method was evaluated on real-world datasets. The results showed that it could effectively improve the object-aware 3D scene reconstruction performance over existing methods.
Spatial adaptive graph convolutional network for skeleton-based action recognition

Abstract In recent years, great achievements have been made in graph convolutional network (GCN) for non-Euclidean spatial data feature extraction, especially the skeleton-based feature extraction. However, the fixed graph structure determined by the fixed adjacency matrix usually causes the problems such as the weak spatial modeling ability, the unsatisfactory generalization performance, the excessively large number of model parameters, and so on. In this paper, a spatially adaptive residual graph convolutional network (SARGCN) is proposed for action recognition based on skeleton feature extraction. Firstly, the uniform and fixed topology is not required in our graph. Secondly, a learnable parameter matrix is added to the GCN operation, which can enhance the model’s capabilities of feature extraction and generalization, while reducing the number of parameters. Therefore, compared with the several existing models mentioned in this paper, the least number of parameters are used in our model while ensuring the comparable recognition accuracy. Finally, inspired by the ResNet architecture, a residual connection is introduced in GCN to obtain higher accuracy at lower computational costs and learning difficulties. Extensive experimental on two large-scale datasets results validate the effectiveness of our proposed approach, namely NTU RGB+D 60 and NTU RGB+D 120.
Marketing Tourism Products in Virtual Reality: Moderating Effect of Product Complexity

Abstract As the tourism industry faces growing competition and digital transformation in the post-pandemic era, virtual reality has emerged as a creative marketing strategy. However, investing in virtual reality may be costly. Evaluating what type of products can generate higher returns through virtual reality is critical. Our study explored the moderating effect of product complexity on the relationship between virtual reality characteristics and behavioral intention. Our results indicated that the relationship between telepresence and consumer trust is stronger for a complex tourism product than a simple product. Firms with limited resources should invest in virtual reality to market complex products.
An efficient regular expression inference approach for relevant image extraction

Traditional approaches for extracting relevant images automatically from web pages are error-prone and time-consuming. To improve this task, operations such as preparing a larger dataset and finding new features are used in the web data extraction approaches. However, these operations are difficult and laborious. In this study, we propose a fully-automated approach based on alignment of regular expressions to automatically extract the relevant images from web pages. The automatically constructed regular expressions has been applied to a classification task for the first time. In this respect, a multi-stage inference approach is developed for generating regular expressions from the attribute values of relevant and irrelevant image elements in web pages. The proposed approach reduces the complexity of the alignment of two regular expressions by applying a constraint on a version of the Levenshtein distance algorithm. The classification accuracy of regular expression approaches is compared with the naive Bayes, logistic regression, J48, and multilayer perceptron classifiers on a balanced relevant image retrieval dataset consisting of 360 image element samples for 10 shopping websites. According to the cross-validation results, the regular expression inference-based classification achieved a 0.98 f-measure with only 5 frequent n-grams, and it outperformed other classifiers on the same set of features. The classification efficiency of the proposed approach is measured at 0.108 ms, which is very competitive with other classifiers.
Twitter Tweet: Sentiment Analysis on Illegal Investment using Naive Bayes Algorithm

In today’s era, investment is in demand by society because of its vast profits. In recent months, Indonesia has been shocked by illegal investment platforms or online trading platforms that are touted as fraudulent investments. Overcoming this problem, sentiment analysis was carried out with the help of social media Twitter. This study aims to find out and analyze people’s sentiments. It is then classified into positive, neutral, and negative sentiments. This study used the naïve Bayes method with orange application. Data collection was taken in March 2022. The survey results obtained 152 positive sentiment data, 244 negative sentiment data, and 175 neutral sentiment data with an accuracy rate of 81.069%. This means that the level of negative sentiment is greater than that of positive sentiment. With this research, it is hoped that it can benefit the public to be more careful in choosing investments.