Reviewerly/exHarmony huggingface dataset:
Neo-Fuzzy Radial-Basis Function Neural Network and Its Combined Learning

The Neo-Fuzzy Radial Basis Function Stacking Neural Network (NF-RBFNN) is a proposed hybrid neural network system that combines traditional RBFNN and neo-fuzzy neuron. The combination aims to create an efficient system that performs well in scenarios with limited data sets or when fast learning is required due to online data acquisition. NF-RBFNN consists of two independent subsystems, which facilitate quick parameter setting and implementation. With the ability to approximate nonlinear functions and handle uncertain information, the neo-fuzzy neuron and traditional radial basis function neural network are known for their respective strengths. By merging these two approaches, the NF-RBFNN is an effective hybrid neural network that can improve approximation properties while handling uncertainties. Ultimately, this simple yet efficient system excels in situations where quick learning and uncertain information handling come into play. With its impressive performance and straightforward implementation, the system holds great potential for a multitude of practical applications in various fields.
A Systematic Literature Review of How to Treat Cognitive Psychology with Artificial Intelligence

The middle-of-the-road nowadays brain cognition is based on artificial intelligence however human subjective emotional and mental state changes cannot simulate the replication of biology. Currently, artificial intelligence does not meet all of our needs due to its limitations, this study's focus on the combination of cognitive psychology and artificial intelligence system would be the research trend of artificial intelligence. The aim of this research is to promote artificial intelligence development and cognitive psychology in terms of emotion, recognition, understanding of human behavior, empathy, and eventually conversion with human being and other artificial intelligence. This research emphasises the importance of possessing the understanding of artificial intelligence, human mental state discrimination, and two typical human interaction system including effective computing and face attraction which is further useful for higher levels of artificial intelligence research. This research also discusses how artificial intelligence is beneficial in the field of psychology and how machine learning techniques have been used to predict the developmental risks of mental health disorders and also detect the level of depression.
Service Robots—An Innovative Sustainability in Rural Banking

Purpose: The purpose of this study is to experimentally test the hypothesized Three-Part Theoretical (TPT) Framework in order to evaluate the impact of the Artificial Intelligence enabled Service Robot, MAYA, among rural banking customers in exploring Customer Loyalty through Customer Satisfaction. The study is based on the Diffusion of Innovation Theory (DOI) and aims to confirm the implementation of such advanced technology in rural banking. Methodology: Multi-Stage Cluster sampling method was adopted to collect the responses from the 385 through Triangulation technique from customers across the rural banking customers across Southern States of India within age group between 21 - 50 years. SAS and Smart PLS 4 were used to apply Simple Percentage Analysis, and Structural Equation Modelling to validate the hypothesis. The key constructs are System Feature, Customer Feature, System Encounter feature, Customer Satisfaction & Customer Loyalty. Results: System feature, Customer feature of the Service Robot, MAYA have a direct positive influence on the Customer Satisfaction but System Encounter feature has a direct negative influence on the Customer Satisfaction. Customer Satisfaction has a direct positive influence on Customer Loyalty with higher predictive capacity. Implications: The benefits of using service robots include increased efficiency, improved safety, and reduced labour costs. It offers personalized assistance to customers, guiding them through the banking process and answering their queries. These findings of the study will facilitate banking managers to enhance rural marketing.
Detailed Review of Challenges in Cloud Computing

With “cloud computing,” a user rents information technology services over a network and has the option to scale up or down depending on their needs. Usually, a third-party provider who also offers cloud computing services takes care of the infrastructure. Among the advantages are scalability, robustness, adaptability, efficiency, and cost-effectiveness. An excellent concept is to offload non-core functions. Unusual business models are offered by cloud computing. Businesses can use IT services with no up-front expenses. Even though cloud computing could have benefits, companies are hesitant to use it because of security concerns. There are numerous problems associated with it which include leakage of data, misuse of user’s personal information, DOS attack and so on. In this review paper, we will discuss several difficulties and problems related to cloud computing security along with the possible solution to these vulnerabilities.
Scenario Modelling in the Context of Foresight Studies

Foresight projects can have different goals and applications, and take various forms and scales. Within the European Foresight Platform framework, monitoring covers thousands of studies. The prevailing number of foresight use scenario approach, and the higher the horizon the higher the share of scenarios in FL studies. Types of scenarios and development procedures are considered in the context of three schools of scenario development, Shell approach, PMT school and La Prospective. Foresight studies of the World Data Center for Geoinformatics and Sustainable Development are presented as cases of FL research’s results implementation in policy-and decision-making. It’s noted the efforts of the COVID-19 Foresight project team of the Center in the area of coronavirus pandemic scenario development. Also, artificial intelligence application prospects for forward-looking studies are highlighted in the context of The Kyiv School of mathematicians and system analytics activity.
Development of PeCiKu TAHeS Educational Media (Hand Washing Habits and Short Nails for Grat and Healthy Children) to Increase Knowledge and Attitudes to Prevent Covid 19 at Tanjungsari 2 Elementary School, Blitar City

Promotion of healthy lifestyle habits of washing hands and short nails in pre-childrenat Tanjungsasi 2 Elementary School is one form of prevention against deviant attitudes andlifestyles. This study used the Pre-Experimental method: The One Group Pretest-PosttestDesign. The research subjects consisted of 60 elementary school students in grades 3 and 4divided into groups of 5 students each as users of the APE Educational Media PeCiKuTAHeS and 30 parents/guardians/teachers, 1 psychologist as an assessor of EducationalMedia Educational Game Tools PeCiKu TAHeS at Tanjungsari 2 Elementary School BlitarCity. The research object was the effectiveness of PeCiKu TAHeS Educational Game Tool(APE) in increasing the hand washing habits and short nails. Collecting data by givingquestionnaires before and after being given treatment to measure the hand washing habitsand short nails. and a media assessment checklist for media eligibility. The results of thepaired sample t-test showed that p = 0.000 (because p &lt; ) then Ho was rejected and Ha wasaccepted, so there was a significant difference in the hand washing habits and short nailsbefore and after being given an intervention in the form of simulation games with PeCiKuTAHeS Educational Game Tool (APE), so it could be concluded that PeCiKu TAHeSEducational Game Tool (APE) was feasible and effective in increasing the habit ofwashing hands and short nails in schoolchild.
Using Text Mining to Analyze Key Topics in Civil Petitions Related to Hazardous Chemicals

Various accident prevention studies and related policies have been developed to ensure public safety when handling and transporting harmful chemical substances. However, these policies primarily focus on improving government-level laws and policies, often overlooking the immediate needs of consumers. Therefore, this study proposed the extraction of meaningful topics and keywords from questions and answers pertaining to the safe handling of harmful chemicals using civil petitions data related to these substances, as posted on the Anti-corruption and Civil Rights Commission platform. The idea is to prioritize these topics in policy formulation. In addition, topic modeling techniques, namely Latent Semantic Analysis and Latent Dirichlet Allocation algorithms, were employed, and the results and implications of each algorithm were compared and analyzed. The main topics identified through the Latent Dirichlet Allocation algorithm were “piping and valve management and inspection,” “manufacturing and storage facility safety,” “outdoor impact assessment,” “damage reduction and accident prevention facility,” and “education and related law.” The results derived from this study are expected to contribute to the development of accident prevention measures by directly addressing the requirements of consumers when it comes to harmful chemical substances.
Improved Metropolis–Hastings algorithms via landscape modification with applications to simulated annealing and the Curie–Weiss model

Abstract In this paper, we propose new Metropolis–Hastings and simulated annealing algorithms on a finite state space via modifying the energy landscape. The core idea of landscape modification rests on introducing a parameter c , such that the landscape is modified once the algorithm is above this threshold parameter to encourage exploration, while the original landscape is utilized when the algorithm is below the threshold for exploitation purposes. We illustrate the power and benefits of landscape modification by investigating its effect on the classical Curie–Weiss model with Glauber dynamics and external magnetic field in the subcritical regime. This leads to a landscape-modified mean-field equation, and with appropriate choice of c the free energy landscape can be transformed from a double-well into a single-well landscape, while the location of the global minimum is preserved on the modified landscape. Consequently, running algorithms on the modified landscape can improve the convergence to the ground state in the Curie–Weiss model. In the setting of simulated annealing, we demonstrate that landscape modification can yield improved or even subexponential mean tunnelling time between global minima in the low-temperature regime by appropriate choice of c , and we give a convergence guarantee using an improved logarithmic cooling schedule with reduced critical height. We also discuss connections between landscape modification and other acceleration techniques, such as Catoni’s energy transformation algorithm, preconditioning, importance sampling, and quantum annealing. The technique developed in this paper is not limited to simulated annealing, but is broadly applicable to any difference-based discrete optimization algorithm by a change of landscape.
Using an Online Data Mining Method for Frequent Pattern Analysis of Invented Spelling

Invented spelling is an important indicator of children's phonetic development and their knowledge of phoneme-morpheme relationships. Research has revealed that students who extensively use invented spellings in their reading and writing were superior to the others on measures of word decoding. This study applies an online association rule learning tool to discover the frequent patterns in various stages of invented spelling of English learners. The rules generalized from these patterns may help teachers to track the progress of a student's phonetic development and create corresponding intervention instructions.
Effective dehazing of night‐time images using open dark channel prior and wavelet transform

Abstract Existing night‐time dehazing methods had been attempting to process light and non‐light source regions based on dark channel prior (DCP). Since the bright and non‐bright regions exhibit different features, the same daytime method cannot be applied to night images because light scatter from the multiple objects non‐uniformly and DCP tends to over‐estimate the depth of the scene making the image unrealistic. To overcome this limitation, wavelet decomposition was performed so that haze remains in the low occurrence region and noise in the high occurrence region and noise was removed by soft thresholding method. In the presented approach, the open DCP (ODCP) transmission map was computed for handling light source regions and estimated transmission was refined to enhance the texture in high‐frequency part. Bilinear interpolation method of fast‐guided filtering and recursive filter in the domain transform was used for edge preservation, enhancement of texture details and smoothness. The dehazed image was constructed by correlating the coefficients of low occurrence part recovered from haze and high occurrence component. The performance analysis was compared against state‐of‐the‐art methods in terms of peak signal‐to‐noise ratio (PSNR) and Structural Similarity Index (SSIM).
Do physicians remember cases? Implications for longitudinal designs in medical research and competency assessment

Background In pathology and other specialties of diagnostic medicine, longitudinal studies and competency assessments often involve physicians interpreting the same images multiple times. In these designs, a washout period is used to reduce the chances that later interpretations are influenced by prior exposure. Objective/s The present study examines whether a washout period between 9 and 39 months is sufficient to prevent three effects of prior exposure when pathologists review digital breast tissue biopsies and render diagnostic decisions: faster case review durations, higher confidence, and lower perceived difficulty. Methods In a longitudinal breast pathology study, 48 resident pathologists reviewed a mix of five novel and five repeated digital whole slide images during Phase 2, occurring 9–39 months after an initial Phase 1 review. Importantly, cases that were repeated for some participants in Phase 2 were novel for other participants in Phase 2. We statistically tested for differences in participants’ case review duration, self-reported confidence, and self-reported difficulty in Phase 2 based on whether the case was novel or repeated. Results No statistically significant difference in review time, confidence, or difficulty as a function of whether the case was repeated or novel in a Phase 2 review occurring 9-39 months after initial viewing; this same result was found in a subset of participants with a shorter (9-14-months) washout. Conclusion These results provide evidence to support the efficacy of at least a 9-months washout period in the design of longitudinal medical imaging and informatics studies to ensure no detectable effect of initial exposure on participant’s subsequent case review.
MD-S3C3

In medical data sharing, the data access control authorities of the sharing entities and computing capabilities of the sharing platforms are asymmetric. This asymmetry leads to poor patient control over their data, privacy disclosure, and difficulties in tracking data sharing. This aarticle proposes a cooperation model of cloud and chain (CMCC) for the secure sharing of medical data. In the CMCC, the power equivalence of blockchain nodes limits the control authority asymmetry between doctors and patients in medical data sharing. Moreover, a cloud server is used to store medical data, and some of the node-side computations are handed over to the cloud, which addresses the asymmetric computing capability asymmetry between the cloud and ordinary nodes. Based on the CMCC, a secure medical data sharing scheme based on proxy re-encryption mechanism is proposed. This scheme realizes secure medical data sharing, especially the patient's complete control of the data. The security and performance analysis show that the proposed scheme outperforms the existing ones.
Towards Distributed and Interactive Multi-cam and Multi-device VR360 Video Experiences

The production and consumption of multimedia content is continuously increasing, and this particularly affects to immersive formats, like VR360 video. Even though significant advances have been witnessed with regard to the processing, delivery and consumption of interactive VR360 video, key challenges and research questions still need to be addressed to efficiently provide interactive multi-camera and multi-user VR360 video services over distributed and heterogeneous environments. This research work aims at providing novel and efficient contributions to overcome existing limitations in this topic. First, it will develop an end-to-end modular web-based VR360 video platform, including the measurement of Quality of Service (QoS) and activity metrics, to be used as a research testbed. Second, it will provide lightweight yet efficient viewport-aware video processing and delivery strategies to dynamically concentrate the video resolution on the user’s viewport, with a single stream and decoding process through the web browser. Third, it will propose innovative encoding, signaling and synchronization solutions to enable an effective support for multi-camera and multi-device VR360 services, in a synchronized manner and with the lowest latency possible. Fourth, it will explore how to effectively provide social viewing scenarios between remote users while watching the same or related VR360 videos, assisted with interactive and guiding techniques.
News Category Classification via Multimodal Fusion Method

Recently digital transformation broadcasting stations classify categories based on a single modality, such as image or captioning, to select and write reporters suitable for the task. In the era of digital journalism, news articles are being organized in multimedia form with a rich mix of image and textual content, highlighting the need for an effective multi-modal analytic approach. We propose that leveraging the complementary information inherent in these diverse modalities through multimodal fusion techniques can significantly improve the performance of news categorization systems. Our study empirically evaluates the effectiveness of early, late, and hybrid fusion technique, providing insights into their strengths and limitations in the context of news category classification. As a result of the experiment, early fusion had an accuracy of 78.13% and an f1 score of 0.7810. Compared to other fusion techniques, the accuracy was at least 0.49% and at most 0.62% effective, and the f1 score was 0.007 effective. The findings of this research not only contribute to a deeper understanding of multimodal fusion techniques in the realm of digital news media but also pave the way for the development of more accurate and robust news categorization systems
ChatGPT-Based Debate Game Application Utilizing Prompt Engineering

This paper1 focuses on the implementation of a debate game using ChatGPT, aiming to investigate the feasibility of incorporating large language models into the educational domain through prompt engineering. The study explores strategies to elicit desired outputs from the GPT model by employing the prompt engineering methodology, as provided by Microsoft.
Forecasting and Detecting Anomalies in ADLs in Single-Resident Elderly Smart Homes

As the ageing population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable the detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we developed and evaluated a Multivariate long short-term memory (LSTM) to learn to forecast activities of daily living and detect anomalous behaviour using motion sensor data in 6 single-resident smart homes of the elderly. We use Mahalanobis distance to identify anomalies based on distance scores to build thresholds. The model's performance in terms of NMAE error values ranges between 2% and 6%. The experimental results show that the performance of LSTM for predicting the direct next activity versus the multiple forecasts is close. The method could identify participants' changing health conditions through the used predictive model and unsupervised anomaly detection method.
Linear growth of circuit complexity from Brownian dynamics

A bstract How rapidly can a many-body quantum system generate randomness? Using path integral methods, we demonstrate that Brownian quantum systems have circuit complexity that grows linearly with time. In particular, we study Brownian clusters of N spins or fermions with time-dependent all-to-all interactions, and calculate the Frame Potential to characterize complexity growth in these models. In both cases the problem can be mapped to an effective statistical mechanics problem which we study using path integral methods. Within this framework it is straightforward to show that the k th Frame Potential comes within ϵ of the Haar value after a time of order t ~ kN + k log k + log ϵ − 1 . Using a bound on the diamond norm, this implies that such circuits are capable of coming very close to a unitary k -design after a time of order t ~ kN . We also consider the same question for systems with a time-independent Hamiltonian and argue that a small amount of time-dependent randomness is sufficient to generate a k -design in linear time provided the underlying Hamiltonian is quantum chaotic. These models provide explicit examples of linear complexity growth that are analytically tractable and are directly applicable to practical applications calling for unitary k -designs.
Efficient adaptive ensembling for image classification

Abstract In recent times, with the exception of sporadic cases, the trend in computer vision is to achieve minor improvements compared to considerable increases in complexity. To reverse this trend, we propose a novel method to boost image classification performances without increasing complexity. To this end, we revisited ensembling, a powerful approach, often not used properly due to its more complex nature and the training time, so as to make it feasible through a specific design choice. First, we trained two EfficientNet‐b0 end‐to‐end models (known to be the architecture with the best overall accuracy/complexity trade‐off for image classification) on disjoint subsets of data (i.e., bagging). Then, we made an efficient adaptive ensemble by performing fine‐tuning of a trainable combination layer. In this way, we were able to outperform the state‐of‐the‐art by an average of 0.5% on the accuracy, with restrained complexity both in terms of the number of parameters (by 5–60 times), and the FLoating point Operations Per Second FLOPS by 10–100 times on several major benchmark datasets.