Reviewerly/exHarmony huggingface dataset:
Convomind: A User-friendly AI Chatbot

Abstract: Artificially intelligent chatbots are computer programs that simulate human conversation by utilizing machine learning and natural language processing (NLP) techniques. AI chatbots are capable of conversing with drug users and providing a variety of services like customer service, superior generation, and tailored recommendations. Due to their capacity to provide round-the-clock customer service, enhance the stoner experience, and lower the costs associated with employing and training human agents, AI chatbots have becoming less and less common. Additionally, AI chatbots can learn from stoner relationships to improve their responses, making them over time actually more effective. Overall, AI chatbots are a valuable tool for companies and individuals looking to automate processes, increase user engagement, and provide their visitors with a personalized experience.
Toxic Comment Detection Using Bidirectional Sequence Classifiers

&lt;p&gt;With the rising surge of online toxicity, automating the identification of abusive language becomes crucial for improving online discourse. This study proposes a deep learning system that efficiently uses multiple labels to classify harmful comments using bi-directional Long Short-Term Memory (LSTM) networks. By leveraging contextual information, the bi-LSTM model achieves state-of-the-art performance in classifying subtle forms of toxicity such as threats, insults, identity hate, and obscenity. The model achieves above 95\% accuracy on benchmark datasets with rigorous data processing, optimized neural architecture, and the utilization of FastText embeddings to handle words that are not in the vocabulary. This technique can automatically filter different levels of toxicity, promoting positive online interactions when integrated into online platforms. The proposed study outlines an end-to-end pipeline incorporating recent NLP advancements and deep contextualized language models to address contemporary challenges in AI-enabled content moderation.&lt;/p&gt;
Toxic Comment Detection Using Bidirectional Sequence Classifiers

With the rising surge of online toxicity, automating the identification of abusive language becomes crucial for improving online discourse. This study proposes a deep learning system that efficiently uses multiple labels to classify harmful comments using bi-directional Long Short-Term Memory (LSTM) networks. By leveraging contextual information, the bi-LSTM model achieves state-of-the-art performance in classifying subtle forms of toxicity such as threats, insults, identity hate, and obscenity. The model achieves above 95\% accuracy on benchmark datasets with rigorous data processing, optimized neural architecture, and the utilization of FastText embeddings to handle words that are not in the vocabulary. This technique can automatically filter different levels of toxicity, promoting positive online interactions when integrated into online platforms. The proposed study outlines an end-to-end pipeline incorporating recent NLP advancements and deep contextualized language models to address contemporary challenges in AI-enabled content moderation.
Sistem Informasi Pengenalan Hewan Berbasis Multimedia Interaktif

Information technology is developing very rapidly, thus making the use of information technology also growing. However, there are still many problems found by teachers in teaching and learning activities with conventional methods. For example, in the learning process, children often joke with their friends or have fun with their toys. The purpose of this study is to find solutions to these problems, either solution is with multimedia system-based learning. Creating an animation using macromedia flash 8 software can attract children's interest to learn about various animals, sounds and shapes so that learning is fun so it can be understood well and not boring. This research produces animation-based learning media that is very useful for teachers as an interesting alternative tool when providing learning material, so that this material can be understood well by children.
Swipe-it!: One-handed Thumb-tip Interaction for Text Editing in AR

We propose an intuitive one-hand text editor called "Swipe-it" for wearable devices in augmented reality (AR) applications. We use thumb-tip interaction, specifically using swiping and tapping gestures for text navigation, selection, and manipulation. Our solution incorporates speed recognition, allowing users to control the amount of text selected based on the speed of their swiping gestures. The system also uses pressure sensing to switch between text navigation and selection modes. A hardware prototype is developed, combining IMU and pressure sensors. Data processing and classification techniques are employed to recognize the thumb-tip gestures accurately. The implementation demonstrates the potential of thumb-tip interaction for text editing in AR environments, providing a portable and intuitive input method.
Joint Median Channel Prior and Edge Enhancement for Single Image Dehazing

Adopting reasonable approaches to process local patches for varying haze conditions is crucial for optimizing the performance in single-image dehazing. Motivated by the success of patch-size techniques in image dehazing, we proposed a novel median channel that respectively implements the median filter on the minimal and maximal color channels depending on the luminance differences in a local patch. In addition, we designed an algorithm to adaptively select the patch size when implementing the median filter determined by the size of the image. The proposed prior can effectively estimate the scene depth and avoid underestimation and local halos. Subsequently, the atmospheric light and transmission can be calculated to restore the degraded images. Furthermore, we developed an edge extraction method and implemented it to enhance local visibility and contrast. The proposed framework was tested by the real-world images and the experimental results presented that our method can perform well in both subjective visual and quantitative evaluations.
The potential of high-performance computing for the Internet of Sounds

High-Performance Computing (HPC) technology is impacting several industries, including the creative industries and those operating in the Internet of Things. However, thus far, scarce attention has been devoted by researchers to the use of such technology in Internet of Sounds (IoS) settings. The IoS is a new area emerging in industry and academia as an extension of the IoT to the sonic domain, both in musical and non-musical contexts. The IoS relates to a distributed network of sound things, which are devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In this position paper, we investigate the integration of HPC technology with the IoS. We illustrate such integration via a set of application scenarios showing how the IoS can benefit from HPC. Finally, we discuss the open challenges ahead of us to implement a successful integration of these two kinds of technology.
Augmented Reality-based demo for Immersive training in horticultural therapy

This demo leverages the innovative concept of Shared Augmented Reality [3] for immersive training in horticultural therapy. The setup involves both the therapist and the patient wearing a HoloLens. Using a smartphone, the therapist sets the parameters of a virtual environment displacing virtual pots. The patient, also wearing HoloLens, is instructed to water the virtual pots using a real watering can (without water) to grow virtual flowers. A countdown timer and 3D sound cues guide the patient through each pot. From the patient’s point of view, this interactive and immersive experience aims to improve his skills in daily living activities while training limb mobility or cognitive functions. From the therapist’s perspective, this framework provides objective assessment parameters while increasing the clinical eye [2]. The presence of real and virtual objects and proper rewards gives the patient a sense of engagement, leading to a more effective training experience.
A Novel Split Rendering XR Framework with Occlusion Support

Extended Reality (XR) applications, especially the revolutionary Mixed Reality technology, with a new level of immersive experiences and novel ways of interactions can reshape our society. However, the required quality of user experience and the desired high visual fidelity pose several challenges. In this demo, we present a novel edge/cloud-based XR platform providing essential features for future applications. During the course of the demo, we highlight the advantages of split rendering and the challenges of occlusion support in dynamic environments. We showcase different solutions, reveal pros/cons of the approaches and analyze their feasibility in different application areas.
Immersive Visualization of The Multifaceted Uncertainties of Hurricane Prediction Ensembles

Visualizations play a crucial role in interpreting hurricane forecasts and facilitating emergency decision-making. However, existing technologies have limitations in effectively conveying the multifaceted uncertainties included in these forecasts. These uncertainties include the spatial spread of forecast tracks, forecasting time, and forward speed. The visualization of hurricane prediction tracks in itself presents a high level of complexity. Incorporating additional visual encoding to represent uncertainties can exacerbate this complexity, making it even more challenging to interpret. To address these challenges, we introduce an immersive uncertainty visualization using stereoscopic rendering. Individual hurricane tracks are rendered as color-coded textured tubes, where color represents forecasting time, and the texture dynamically changes to represent the uncertainty of forward speed. To prevent visual clutter from overlapping tracks, we map time-specific predicted locations along these tracks to varied heights, thereby enhancing depth perception and facilitating an improved interpretation of the visual encodings. We conducted an experimental study to evaluate the effectiveness of our approach, examining participants’ estimations of the distributions of these uncertainties. The results reveal that participants were generally successful in interpreting these time-specific uncertainties, and exhibited greater accuracy in estimating distributions of high and low forward speeds compared to medium speed.
Aesthetic thinking on piano performance teaching in universities from the perspective of artistic philosophy

This article aims to explore the aesthetic thinking of piano performance teaching in universities under the vision of artistic philosophy, with a view to improving the aesthetic perception and appreciation ability of contemporary college students, cultivating students' artistic thinking, and promoting the integration of performance skills and emotions.
Unlinkability and Interoperability in Account-Based Universal Payment Channels

Payment channels allow a sender to do multiple transactions with a receiver without recording each single transaction on-chain. While most of the current constructions for payment channels focus on UTXO-based cryptocurrencies with reduced scripting capabilities (e.g., Bitcoin or Monero), little attention has been given to the possible benefits of adapting such constructions to cryptocurrencies based on the account model and offering a Turing complete language (e.g., Ethereum). The focus of this work is to implement efficient payment channels tailored to the capabilities of account-based cryptocurrencies with Turing-complete language support in order to provide scalable payments that are interoperable across different cryptocurrencies and unlinkable for third-parties (e.g., payment intermediaries). More concretely, we continue the line of research on cryptocurrency universal payment channels ( $$\textsf{UPC}$$ ) which facilitate interoperable payment channel transactions across different ledgers in a hub-and-spoke model, by offering greater scalability than point-to-point architectures. Our design proposes two different versions, $$\textsf{UPC}$$ and $$\textsf{AUPC}$$ . For $$\textsf{UPC}$$ we formally describe the protocol ideas sketched in previous work and evaluate our proof-of-concept implementation. Then, $$\textsf{AUPC}$$ further extends the concept of universal payment channels by payment unlinkability against the intermediary server.
Phishing Website Detection using Machine Learning: A Comprehensive Study

Phishing attacks continue to be a serious danger to consumer privacy and internet safety. Attackers use false websites that look like authentic ones to steal sensitive data, including login credentials and financial information. In the study, different machine learning techniques for categorizing phishing websites are evaluated, including decision trees,logistic regression,Multinomial NB.This study also explores the potential integration of the generated models into online browsers and security systems, which would offer real-time defense against phishing assaults.The results of this study help to improve internet security, protect user data, and lessen the effect of phishing assaults on the digital world.
Generation of Modern Pulse Radar Signals for Electronic Warfare Applications

An innovative approach is presented to generate modern pulse radar signals specifically designed to meet the demands of electronic warfare (EW) applications in this article. In EW applications, pulse descriptor words (PDWs) are used to represent the characteristics of intercepted emitter signals. With the applications, the architecture of the PDW generator was developed in this study. The user-defined parameters and modulations were also proposed for meeting the demands of EW operations. 1 to 5 PDW parameters were chosen, and 7 pulse repetition interval (PRI) modulations. In addition, the PDW sequences included scenarios with 3 non-ideal pulses. We generated interleaving pulsed trains that incorporated multiple radar modulation techniques. The pulse sequence from each radar was generated based on the specific characteristics of that radar. The results offered a more detailed description of radar pulse signals on the modern battlefield, making them valuable for ESM deinterleaving research.
Factors influencing elementary school students' academic stress in the COVID-19 situation

Objectives The main purpose of this study is to analyze variables that affect the academic stress of elementary school students during the COVID-19 period.&#x0D; Methods To In order to analyze the research problem, data surveyed the 12th in 2019 and the 13th data surveyed in 2020 of Panel Study on Korean Children were used to analyze using analysis methods such as ANOVA, paired-samples t-test and regression analysis.&#x0D; Results Looking at the results of the paired-sample t-test analysis of students' academic stress and monthly average private education expenses during the pre-COVID-19 period and during the COVID-19 period, students' academic stress level and average monthly private education expenditure during the COVID-19 period were statistically significantly higher than before the period before COVID-19. As a result of analyzing the effects on elementary school students' academic stress, variables such as student persistence, life satisfaction, academic performance evaluation, and learning activity adaptation significantly reduced elementary school students' academic stress. On the other hand, parental achievement pressure and monthly average private education expenses increased the academic stress of elementary school students statistically significantly.&#x0D; Conclusions We analyzed the variables that affect elementary school students' academic stress during the COVID-19 period, and based on the analyzed results, policy suggestions and follow-up research suggestions were made.
Few-Shot and Transfer Learning with Manifold Distributed Datasets

A manifold distributed dataset with limited labels makes it difficult to train a high-mean accuracy classifier. Transfer learning is beneficial in such circumstances. For transfer learning to succeed, the target and base datasets should have a similar manifold structure. A novel method is presented in this paper for determining the similarity between two manifold structures. To determine whether target and base datasets have similar manifolds and are suitable for transfer learning, this method can be used. A novel few-shot algorithm is then presented that uses transfer learning to classify manifold distributed datasets with a limited number of labels. Using the base and target datasets, the manifold structure and its relevant label distribution are learned. Using this information in combination with the few labels and unlabeled data from the target dataset, we can develop a classifier with high mean accuracy.
Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.
Cloud Computing Based Online Video Rhythmic Gymnastics Course Management

Abstract In this paper, we utilize cloud computing to measure the best features in the Rhythmic Gymnastics dataset and compare them to obtain the most relevant feature information. By measuring the degree of uncertainty of random variables and calculating the course recommendation of target students relative to those in the subgroups, the clustering results of Rhythmic Gymnastics students were integrated with the target students’ performance information to derive the probability of Rhythmic Gymnastics courses appearing in the subgroups. Weight control, physical fitness, and good shape were significantly altered, and 95.66% of the Rhythmic Gymnastics students who took the course felt more flexible. Integration of online videos from cloud computing with the Rhythmic Gymnastics course makes full use of various media forms to provide more learning opportunities for students.
FAUSTMapper: Facilitating Complex Mappings for Smart Musical Instruments.

Smart Musical Instruments (SMIs) are digital instruments with key traits such as connectivity, leveraging sensors and the use of embedded intelligence in order to create new interactions between musicians, their instruments and audiences. Mappings are what define the connection between an instrument’s inputs and outputs. Literature shows that complex mappings allow musicians to have high levels of control over their instrument, and that mappings also have a significant effect on an audience’s perception of the instrument. Thus, creating complex mappings is a necessary next step when building effective SMIs. In this work, we present a set of lightweight bindings between FAUST and libmapper, two popular tools in the digital musical instruments space. We demonstrate how these bindings enable users to quickly create complex mappings between their own inputs and arbitrary FAUST synthesizers at run-time, allowing researchers and musicians to focus on the creation of mappings that best suit their individual requirements for SMIs.
A Taxonomy of QKA Protocols Based on the BB84

In Quantum Key Agreement (QKA) protocol, two or more participants agree to share a quantum key. Participants must meet the following conditions: (1) Can be toggled per participant. Important decisions are not made by individual participants. (2) Only participants can access the key, which is not viewable by non-participants while unauthorized acquisition of keys is impossible. Key agreement protocols are used to generate cryptographic keys in many modern applications such as IoT applications, healthcare systems, vehicle communications, intelligent networks, satellite communications, and cloud applications. To resist quantum attacks, several security protocols based on quantum physics principles have been proposed to address various security issues. The main goal of this research is to provide fundamental background information along with a detailed analysis of the theoretical aspects of QKA. The emphasis is particularly on various kinds of multiparty quantum key agreement protocols considering their design and effectiveness, with a specific spotlight on the utilization of the BB84 protocol.
A New Design of a Multi-Link Quadrotor

In this paper, we propose a new design of a multilink quadrotor capable of traversing narrow spaces and transporting objects by changing its shape to adapt the geometry while flying. Unlike traditional quadrotors, which need an arm manipulator for transportation, this design makes it possible to reduce its weight. The proposed aerial multilink quadrotor can fly and modify its orientation and position in space without affecting the difference between lift forces provided by its propellers. Instead, the attitude movements can be achieved by adjusting the angles of its links, which allows the optimization of the consumed energy. Furthermore, it can adapt its morphology in flight according to the assigned task to find the most suitable configuration. We have proposed several configurations, each of which can be used to perform the task efficiently and optimally, and identify singular configurations that should be avoided that could render our system uncontrollable.
Augmented Reality Rehabilitative and Exercise Games (ARREGs): A Systematic Review and Future Considerations

Augmented Reality (AR) and exergames have been trending areas of interest in healthcare spaces for rehabilitation and exercise. This work reviews 25 papers featuring AR rehabilitative/exercise games and paints a picture of the literature landscape. The included results span twelve years, with the oldest paper published in 2010 and the most recent work in 2022. More specifically, this work contributes a bank of representative ARREGs and a synthesis of measurement strategies for player perceptions of Augmented Reality Rehabilitative and Exercise Game (ARREG) experiences, the elements that comprise the exergame experience, the intended use cases of ARREGs, whether participants are actually representative users, the utilized devices and AR modalities, the measures used to capture rehabilitative success, and the measures used to capture participant perceptions. Informed by the literature body, our most significant contribution is nine considerations for future ARREG development.