Reviewerly/exHarmony huggingface dataset:
Deep Learned Singular Residual Network for Super Resolution Reconstruction

Single image super resolution (SISR) techniques produce images of high resolution (HR) as output from input images of low resolution (LR).Motivated by the effectiveness of deep learning methods, we provide a framework based on deep learning to achieve super resolution (SR) by utilizing deep singular-residual neural network (DSRNN) in training phase.Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs.Singular value decomposition (SVD) is applied to each LR-residual image pair to decompose into subbands of low and high frequency components.Later, DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the network.With fewer layers in DSRNN, the influence of exploding gradients is reduced.This speeds up the learning process and also improves accuracy by using skip connections.The trained DSRNN parameters yield residuals to recover the HR subbands in the testing phase.Experimental analysis shows that the proposed method results in superior performance to existing methods in terms of subjective quality.Extensive testing results on popular benchmark datasets such as set5, set14, and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics.
Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic.Social media has been shown to influence the low acceptance of vaccines.This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual's sensitivities and feelings that lead to achievement.This work proposes a method to analyze the opinion of an individual's tweet about the COVID-19 vaccines.This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm.First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment.The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection.The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing.The proposed approach outperformed considered algorithms in terms of accuracy.Additionally, we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm.It shows that augmentation improves the overall accuracy of tweet analysis.After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset.
Privacy Data Management Mechanism Based on Blockchain and Federated Learning

Due to the extensive use of various intelligent terminals and the popularity of network social tools, a large amount of data in the field of medical emerged.How to manage these massive data safely and reliably has become an important challenge for the medical network community.This paper proposes a data management framework of medical network community based on Consortium Blockchain (CB) and Federated learning (FL), which realizes the data security sharing between medical institutions and research institutions.Under this framework, the data security sharing mechanism of medical network community based on smart contract and the data privacy protection mechanism based on FL and alliance chain are designed to ensure the security of data and the privacy of important data in medical network community, respectively.An intelligent contract system based on Keyed-Homomorphic Public Key (KH-PKE) Encryption scheme is designed, so that medical data can be saved in the CB in the form of ciphertext, and the automatic sharing of data is realized.Zero knowledge mechanism is used to ensure the correctness of shared data.Moreover, the zero-knowledge mechanism introduces the dynamic group signature mechanism of chosen ciphertext attack (CCA) anonymity, which makes the scheme more efficient in computing and communication cost.In the end of this paper, the performance of the scheme is analyzed from both asymptotic and practical aspects.Through experimental comparative analysis, the scheme proposed in this paper is more effective and feasible.
Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
Analysis of psychological characteristics and emotional expression based on deep learning in higher vocational music education

Sentiment analysis is one of the important tasks of online opinion analysis and an important means to guide the direction of online opinion and maintain social stability. Due to the multiple characteristics of linguistic expressions, ambiguity, multiple meanings of words, and the increasing speed of new words, it is a great challenge for the task of text sentiment analysis. Commonly used machine learning methods suffer from inadequate text feature extraction, and the emergence of deep learning has brought a turnaround for this purpose. In this paper, we investigate the problem of text sentiment analysis using methods related to deep learning. In order to incorporate user and product information in a more diverse way in the model, this paper proposes a model based on a deep bidirectional long-and short-term memory network-self-attention mechanism-custom classifier. The model first identifies contextual associations and acquires deep text features through a deep bidirectional long- and short-term memory network and then captures important features in the text using a self-attentive mechanism. The model finally combines user information and product information to build a custom classifier module and uses context-aware attention mechanisms to assign specific parameters to user information and product information, which improves the performance of the model on public datasets compared with current common models. The results show that the accuracy of the algorithm in this paper is high, and it is about 5% lower than the traditional algorithm. The method can reduce the number of iterations and the running time of the algorithm.
A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice.
A randomized prospective study of a hybrid rule- and data-driven virtual patient

Abstract Randomized prospective studies represent the gold standard for experimental design. In this paper, we present a randomized prospective study to validate the benefits of combining rule-based and data-driven natural language understanding methods in a virtual patient dialogue system. The system uses a rule-based pattern matching approach together with a machine learning (ML) approach in the form of a text-based convolutional neural network, combining the two methods with a simple logistic regression model to choose between their predictions for each dialogue turn. In an earlier, retrospective study, the hybrid system yielded a nearly 50% error reduction on our initial data, in part due to the differential performance between the two methods as a function of label frequency. Given these gains, and considering that our hybrid approach is unique among virtual patient systems, we compare the hybrid system to the rule-based system by itself in a randomized prospective study. We evaluate 110 unique medical student subjects interacting with the system over 5,296 conversation turns, to verify whether similar gains are observed in a deployed system. This prospective study broadly confirms the findings from the earlier one but also highlights important deficits in our training data. The hybrid approach still improves over either rule-based or ML approaches individually, even handling unseen classes with some success. However, we observe that live subjects ask more out-of-scope questions than expected. To better handle such questions, we investigate several modifications to the system combination component. These show significant overall accuracy improvements and modest F1 improvements on out-of-scope queries in an offline evaluation. We provide further analysis to characterize the difficulty of the out-of-scope problem that we have identified, as well as to suggest future improvements over the baseline we establish here.
Model-Free Deep Recurrent Q-Network Reinforcement Learning for Quantum Circuit Architectures Design

Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum systems in the Noisy Intermediate-Scale Quantum (NISQ) era. Classical agent-based artificial intelligence algorithms provide a framework for the design or control of quantum systems. Traditional reinforcement learning methods are designed for the Markov Decision Process (MDP) and, hence, have difficulty in dealing with partially observable or quantum observable decision processes. Due to the difficulty of building or inferring a model of a specified quantum system, a model-free-based control approach is more practical and feasible than its counterpart of a model-based approach. In this work, we apply a model-free deep recurrent Q-network (DRQN) reinforcement learning method for qubit-based quantum circuit architecture design problems. This paper is the first attempt to solve the quantum circuit design problem from the recurrent reinforcement learning algorithm, while using discrete policy. Simulation results suggest that our long short-term memory (LSTM)-based DRQN method is able to learn quantum circuits for entangled Bell–Greenberger–Horne–Zeilinger (Bell–GHZ) states. However, since we also observe unstable learning curves in experiments, suggesting that the DRQN could be a promising method for AI-based quantum circuit design application, more investigation on the stability issue would be required.
ACF: An Armed CCTV Footage Dataset for Enhancing Weapon Detection

Thailand, like other countries worldwide, has experienced instability in recent years. If current trends continue, the number of crimes endangering people or property will expand. Closed-circuit television (CCTV) technology is now commonly utilized for surveillance and monitoring to ensure people's safety. A weapon detection system can help police officers with limited staff minimize their workload through on-screen surveillance. Since CCTV footage captures the entire incident scenario, weapon detection becomes challenging due to the small weapon objects in the footage. Due to public datasets providing inadequate information on our interested scope of CCTV image's weapon detection, an Armed CCTV Footage (ACF) dataset, the self-collected mockup CCTV footage of pedestrians armed with pistols and knives, was collected for different scenarios. This study aimed to present an image tilling-based deep learning for small weapon object detection. The experiments were conducted on a public benchmark dataset (Mock Attack) to evaluate the detection performance. The proposed tilling approach achieved a significantly better mAP of 10.22 times. The image tiling approach was used to train different object detection models to analyze the improvement. On SSD MobileNet V2, the tiling ACF Dataset achieved an mAP of 0.758 on the pistol and knife evaluation. The proposed method for enhancing small weapon detection by using the tiling approach with our ACF Dataset can significantly enhance the performance of weapon detection.
DELINEATION OF BLOCKCHAIN TECHNOLOGY IN FINANCE: A SCIENTOMETRIC VIEW

Blockchain technology has attracted a lot of attention due to its revolutionary potential to upend the established economic structures, leading to a deluge of literature on the topic. Academics and business leaders alike are eager to see this technology used in the financial industry, although its full potential and limitations are currently unknown. To bridge this knowledge gap, 3312 records from the Dimensions database were obtained between 2014 and 2022 and used for bibliometrics and network analysis on the subject of blockchain technology in the financial sector. This analysis articulates the various potentials of blockchain literature as well as its adoption by various countries and authors, collaboration patterns, and applications of blockchain in finance using the visual mapping technique provided by VOSviewer. The top two innovators in the field, China and the USA, have used blockchain more extensively in financial research. Furthermore, the results show that the coverage of blockchain in finance has exploded in the last three years. Although India ranks third in documentation, behind China and the USA, it is lacking in citations and networking opportunities. The results, which identify important journals and authors in this field, will aid future researchers in better understanding the literature and conducting a PRISMA-based systematic review. This research is unique because it is the first bibliometric study to use the Dimensions AI database to look into how blockchain technology can be used in the financial sector.
Superconducting quantum circuit of NOR in quantum annealing

The applicability of quantum annealing to various problems can be improved by expressing the Hamiltonian using a circuit satisfiability problem. We investigate the detailed characteristics of the NOR/NAND functions of a superconducting quantum circuit, which are the basic building blocks to implementing various types of problem Hamiltonians. The circuit is composed of superconducting flux qubits with all-to-all connectivity, where direct magnetic couplers are utilized instead of the variable couplers in the conventional superconducting quantum circuit. This configuration provides efficient scalability because the problem Hamiltonian is implemented using fewer qubits. We present an experiment with a complete logic operation of NOR/NAND, in which the circuit produces results with a high probability of success for arbitrary combinations of inputs. The features of the quantum circuit agree qualitatively with the theory, especially the mechanism for an operation under external flux modulation. Moreover, by calibrating the bias conditions to compensate for the offset flux from the surrounding circuit, the quantum circuit quantitatively agrees with the theory. To achieve true quantum annealing, we discuss the effects of the reduction in electric noise in quantum annealing.
<scp>CA‐MLBS</scp>: content‐aware machine learning based load balancing scheduler in the cloud environment

Abstract Cloud computing is the on‐demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance. Several methods have been proposed in the literature for effective load balancing, including heuristic, meta‐heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta‐heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI‐assisted hybrid approach called Content‐aware Machine Learning based Load Balancing Scheduler (CA‐MLBS) is proposed. The scheduling system CA‐MLBS combines machine learning and meta‐heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA‐MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively.
A survey of semantic web (Web 3.0), its applications, challenges, future and its relation with Internet of things (IoT)

The Semantic Web (Web 3.0) is an advancement of the existing web in which knowledge is given well-defined importance, allowing people and machines to operate better. The Semantic Web is the next step in the evolution of the Web. The semantic web improves online technologies in need of generating, distributing, and linking material. In literature, multiple surveys have been done on the semantic web (Web 3.0), but those surveys are limited to some specific topics. According to the best of our understanding, none of the surveys provides a comprehensive study about the applications, challenges, and future of the semantic web along with its relationship with the Internet of things (IoT). The previous surveys focused on the Web 3.0 without touching on applications or challenges or focused on only the application prospect of the web 3.0, focused on the just the challenges, or focused on web 3.0 relationship with either internet of things or knowledge graphs but failed to touch the other important factors i.e., failed to provide comprehensive web 3.0 survey. This survey paper covers the gaps created from the previous survey papers in the same field and provides a comprehensive survey about web 3.0, a comparison between web 1.0, 2.0, and 3.0, the study of application and challenges in web 3.0, the relationship between web 3.0 with IoT and knowledge graph. Moreover, it focuses on the evolution of the web, and semantic web along with an explanation of the various layers, ontology tools, and semantic web tools with their comparison and semantic web service search. Despite all the shortcomings and challenges, the semantic web is moving in the right direction, and it is the future of the web.
Unknown Input Reconstruction from Temporal Activity Patterns of Thermosensitive Neuronal Ensembles using Reservoir Computing*

The paper addresses the problem of the unknown temperature reconstruction from the neuronal signal generated by networks of thermosensitive neurons modeled by the Hodgkin-Huxley formalism. We show that the instantaneous frequencies of the neurons do not provide sufficient information for the precise temperature reconstruction and that the temporal patterns of the instantaneous frequencies should be taken into account. For this purpose, we augment the considered network of thermosensitive neurons with a multi-layered artificial neural network (ANN) and train it to reconstruct the unknown piecewise-constant input temperature using a supervised learning technique. The input layer of the ANN receives the discretized (in time) trajectories of the instantaneous frequencies of neurons over a certain time interval. Interestingly more rich information containing the time evolution of membrane potentials in the input layer does not lead to any improvement of the unknown input reconstruction. This observation is in accordance with one of the fundamental principles of neuroscience stating that, except for a few highly specific contexts, information in neural systems is encoded in the temporal rather than voltage characteristics of action potentials. Finally, we benchmark the performance of the proposed reconstruction scheme against different types of input signals.