Reviewerly/exHarmony huggingface dataset:
Exploring Students’ Hands-On Performance, Attitudes, and Usability with Arduino Modular Boards

Utilizing Arduino development boards for learning microcontroller circuits is a prevalent practice across various educational levels. Nevertheless, the literature offers limited insights into the impact of these boards on student performance and attitudes. Therefore, this paper aims to investigate the performance of 58 university students in learning microcontroller circuits with modular boards designed for Arduino through a series of 4 exercises. Specifically, students’ performance is assessed through pre-tests and post-tests, in three learning units: (a) microcontroller, (b) coding, and (c) circuit. Additionally, the study captures students’ attitudes and measures their perceived usability of modular boards. For this purpose, the students completed a specially designed attitude questionnaire and the system usability scale (SUS) questionnaire. Statistical analysis is conducted using t-tests, ANOVA, and ANCOVA, along with bootstrapping. The findings reveal statistically significant differences between pre-tests and post-tests in all cases. Among the three learning units, the use of modular boards appears to have the most significant impact on coding. Based on students’ responses, the SUS results indicate that modular boards appear to be a quite usable approach for teaching microcontrollers. Finally, students generally express positive attitudes toward modular boards.
Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis

Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how attributes of social media posts (i.e., features) influence safety evaluations of mock posts selling illicit substances. 440 participants were recruited online for self-reporting use or purchase of controlled substances or prescription medicines recreationally. The following attributes were tested: drug packaging, drug offerings, profile photo of seller, payment info provided, and use of emojis. Results: Packaging was ranked the most important attribute (Average Importance =43.68, Offering=14.94, Profile=13.86, Payment=14.11, Emoji=13.41), with posts that displayed drugs in pill bottles assessed as the most safe. Attribute levels for advertising multiple drugs, having a blank profile photo, including payment information, and including emojis also ranked higher in perceived safety. Rankings were consistent across tested demographic factors (i.e., gender, age, and income). Survey results show that online pharmacies were most likely to be perceived as safe for purchasing drugs and medications. Additionally, those who were younger in age, had higher income, and identified as female were more likely to purchase from a greater number of platforms. Conclusions: These findings can assist in developing more precise content moderation for platforms seeking to address this ongoing threat to public safety.
Three-wave-mixing quantum-limited kinetic inductance parametric amplifier operating at 6 T near 1 K

Parametric amplifiers are essential in modern quantum technology, for enhancing weak signals with the minimal added noise of half a photon, and for working at higher temperatures and magnetic fields than Josephson junctions can handle. The authors present a kinetic inductance parametric amplifier (KIPA) that excels in dynamic range, operational temperature, and magnetic field resilience, and offers quantum-limited amplification. This KIPA's robust performance and compatibility promise transformative impacts on quantum information processing and low-temperature quantum research.
Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Artificial neural network models, crucial for accurate predictions, should be meticulously designed for specific problems using deep learning-based algorithms. In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. The consolidation features of fine-grained soil have a significant impact on the stability of structures, particularly in terms of long-term stability. Precise prediction of soil consolidation under planned structures is vital for effective foundation design. The compression index (Cc) is an important parameter used in predicting consolidation settlement in soils. Therefore, this study examines the use of deep learning techniques, which are types of artificial neural network algorithms with deep layers, in predicting compression index (Cc) in geotechnical engineering. Four neural network models with different architectures and hyperparameters were modeled and evaluated using performance metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset contains 916 samples with variables such as natural water content (w), liquid limit (LL), plasticity index (PI), and compression index (Cc). This approach allows the results of soil consolidation tests to be seen more quickly at less cost, although predictively. The findings demonstrate that deep learning models are an effective tool in predicting consolidation of fine-grained soil and offering significant opportunities for applications in geotechnical engineering. This study contributes to a more accurate prediction of soil consolidation, which is critical for the long-term stability of structural designs.
Multi-class Facial Emotion Expression Identification Using DL-Based Feature Extraction with Classification Models

Abstract Facial expression detection from images and videos has recently gained attention due to the wide variety of applications it has found in the field of computer vision such as advanced driving assistance systems (ADAS), augmented and virtual reality (AR/VR), video retrieval, and security systems. Facial terms, body language, hand gestures, and eye contact have all been researched as a means of deciphering and understanding human emotions. Automated facial expression recognition (FER) is a significant visual recognition procedure because human emotions are a worldwide signal used in non-verbal communication. The six primary universal manifestations of emotion are characterized as happiness, sadness, anger, contempt, fear, and surprise. While the accuracy of deep learning (DL)-based approaches has improved significantly across many domains, automated FER remains a difficult undertaking, especially when it comes to real-world applications. In this research work, two publicly available datasets such as FER2013 and EMOTIC are considered for validation process. Initially, pre-processing includes histogram equalization, image normalization and face detection using Multi-task Cascaded Convolutional Network (MT-CNN) is used. Then, DL-based EfficinetNetB0 is used to extract the features of pre-processed images for further process. Finally, the Weighted Kernel Extreme Learning Machine (WKELM) is used for classification of emotions, where the kernel parameters are optimized by Red Fox Optimizer (RFO). From the experimental analysis, the proposed model achieved 95.82% of accuracy, 95.81% of F1-score and 95% of recall for the testing data.
Review on Panoptic Segmentation of Images with Text-to-Image and Image-to-Image Diffusion Models

The strategic integration of panoptic segmentation and diffusion models within a comprehensive project is explored in detail in this review study, with a focus on the respective contributions and usefulness of each approach. Selected due to their capacity for dynamic process analysis, the diffusion models are expected to reveal complex patterns and behaviors in a variety of applications, providing fine-grained understanding of the temporal evolution of things. In addition, the project's analytical depth is enhanced by the intentional inclusion of panoptic segmentation, which highlights its critical role in obtaining total scene interpretation. Results are expected that will demonstrate the project's usefulness in reconstructing distorted images and classifying them into labels in order to improve comprehension for average people interacting with complex visual data. A comprehensive analysis of the methodology, applications, difficulties, and potential applications is included in the paper's conclusion, providing insight into the strategic combination of diffusion models with panoptic segmentation. The integration of perspectives from many scholarly articles enhances our comprehension and offers significant insights on the intentional use of these models. This collaborative method promotes continual learning and improvement in the use of diffusion models and panoptic segmentation across multiple disciplines, while also advancing grasp of dynamic processes and scene interpretation. We also in the end tend to provide a detailed and systematic review on all the current models and their papers that are published to provide a clear observation on where we stand on image generation and segmentation
Advanced Access Controls and Deduplication for Privacy in Multimedia Cloud Computing

This work provides a privacy-preserving multi-dimensional media sharing system, SMACD, for portable cloud computing scenarios within the context of widespread media sharing enabled by cloud computing and devices. Attribute-based encryption is used to jumble each media layer, ensuring media privacy and granular access control. To reduce the complexity of the get-to arrangement and adapt to the properties of multi-dimensional media, a multi-level get-to arrangement development with a secret-sharing plot is presented. For both intra-server and inter-server deduplication, decentralized key servers are offered. With cloud computing, databases and application software are moved to sizable data centers, where data and service management may not be entirely reliable. However, this special feature brings up a number of new, poorly understood security challenges. Due to the fact that both user data and applications are on provider premises, cloud computing raises data security problems as neither is entirely contained on the user's machine. Although clouds often have a single architecture, they can have numerous customers with various needs. The solution provided by all cloud providers is to use encryption methods to encrypt the data. To solve every issue, there's also a potential that the cloud service is unreliable
Smart Health Recommendation using Vitals

The term "artificial intelligence" (AI) in healthcare means the application of machine-learning algorithms and software to mimic how humans think in the analysis, presentation, and comprehension of intricate medical and health care data, or to outperform human capabilities by offering novel approaches to illness diagnosis, treatment, and prevention. New customer wellbeing gadgets are being created to effortlessly screen various physiological boundaries on an ordinary premise. A considerable lot of these crucial sign estimation gadgets concentrated on in a clinical setting as now spread broadly all through the purchaser market. The purpose of this investigation was to examine the exactness and accuracy of pulse (HR), blood pressure (BP) and estimations by taking dataset through smartwatch. This paper provides information and methods employed in the health monitoring system utilizing K-means Clustering tasks such as monitoring blood pressure or ECG readings, Db scan for arranging unstructured data, SVM for Forecasting healthcare solutions and human health patterns and creating medical answers by combining devices, instruments, and cases. Neural Network for enhancing medical hardware, software, and instruments. Forecasting Healthcare Solutions for Utilizing machine learning for creating predictive healthcare solutions. Overall the paper gives detail knowledge about the technique used for a machine learning and artificial intelligence-based health recommendation system
Quantitative Nonclassicality of Mediated Interactions

In a plethora of physical situations, one can distinguish a mediator—a system that couples other, noninteracting, systems. Often, the mediator itself is not directly accessible to experimentation, yet it is interesting and sometimes crucial to understand if it admits nonclassical properties. An example of this sort that has recently been enjoying considerable attention is that of two quantum masses coupled via a gravitational field. It has been argued that the gain of quantum entanglement between the masses indicates nonclassicality of the states of the whole tripartite system. Here, we focus on the nonclassical properties of the involved interactions rather than the states. We derive inequalities the violation of which indicates noncommutativity and nondecomposability (open-system generalization of noncommuting unitaries) of interactions through the mediators. The derivations are based on properties of general quantum formalism and make minimalistic assumptions about the studied systems; in particular, the interactions can remain uncharacterized throughout the assessment. Furthermore, we also present conditions that solely use correlations between the coupled systems, excluding the need to measure the mediator. Next, we show that the amount of violation places a lower bound on suitably defined degree of nondecomposability. This makes the methods quantitative and at the same time experiment ready. We give applications of these techniques in two different fields: for detecting the nonclassicality of gravitational interaction and in bounding the Trotter error in quantum simulations.Received 24 March 2023Accepted 30 November 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010318Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum correlations, foundations & formalismQuantum foundationsQuantum information theoryQuantum Information, Science & Technology
Review on Smart Evaluation of Descriptive Answer Sheets

This descriptive abstract summarizes a thorough examination into the use of smart technology for answer sheet evaluation. The study explores how to automate the grading process using Artificial Intelligence, Machine Learning and other algorithms to improve efficiency and objectivity while evaluating student responses. Examined are several smart assessment systems, stressing attributes such as adaptive learning processes, pattern recognition and natural language processing. The abstract delves into the possible advantages, obstacles and ramifications linked to the implementation of intelligent response sheet assessment techniques in educational environments. The abstract offers insights into the changing landscape of assessment methodologies through a synthesis of recent research findings, illuminating the revolutionary potential of intelligent systems in reshaping education in the future
Rep-YOLO: an efficient detection method for mine personnel

The detection of underground personnel is one of the key technologies in computer vision. However, this detection technique is susceptible to complex environments, resulting in low accuracy and slow speed. To accurately detect underground coal mine operators in complex environments, we combine the underground image features with K-means++ clustering anchor frames and propose a new Re-parameterization YOLO (Rep-YOLO) detection algorithm. First, the Criss-Cross-Vertical with Channel Attention (CVCA) mechanism is introduced at the end of the network to capture the Long-Range Dependencies (LRDs) in the image. This mechanism also emphasizes the significance of different channels to enhance image processing performance and improve the representation ability of the model. Second, the new Deep Extraction of Re-parameterization (DER) backbone network is designed, which adopts the re-parameterization structure to reduce the number of parameters and computation of the model. Additionally, each DER-block fuses different scales of features to enhance the accuracy of the model's detection capabilities. Finally, Rep-YOLO is optimized using a slim-neck structure, which reduces the complexity of the Rep-YOLO while maintaining sufficient accuracy. The results showed that the Rep-YOLO model proposed in this paper achieved an accuracy of $$87.5\%$$ , a recall rate of $$77.2\%$$ , an Average Precision (AP) of $$83.1\%$$ , and a Frame Per Second (FPS) of 71.9. Compared to eight different models, the recall, AP50, and FPS of the Rep-YOLO model were improved. The research shows that the Rep-YOLO model can provide a real-time and efficient method for mine personnel detection. Source code is released in https://github.com/DrLSB/Rep-YOLO .
Efficient knowledge model for whale optimization algorithm to solve large-scale problems

In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage.
Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29%.
Claude 2.0 Large Language Model: tackling a real-world classification problem with a new Iterative Prompt Engineering approach

In the last year, Large Language Models (LLMs) have transformed the way of tackling problems, opening up new perspectives in various works and research fields,due to their ability to generate and understand human languages. In this regard, the recent release of Claude 2.0 has contributed to the processing of more complex prompts. In this scenario, the goal of this paper is to evaluate the effectiveness of Claude 2.0 in a specific classification task. In particular, we considered the Forest cover-type problem, concerning the prediction of a cover-type value according to the geospatial characterization of target worldwide areas. To this end, we propose a novel iterative prompt template engineering approach, which integrates files by exploiting prompts and evaluates the quality of responses provided by the LLM. Moreover, we conducted several comparative analyses to evaluate the effectiveness of Claude 2.0 with respect to online and batch learning models. The results demonstrated that, although some online and batch models performed better than Claude 2.0, the new iterative prompt engineering approach improved the quality of responses, leading to better performance with increases ranging from 14% to 32% in terms of accuracy, precision, recall, and F1-score.