Reviewerly/exHarmony huggingface dataset:
Enabling Semi-Structured Knowledge Access via a Question-Answering Module in Task-oriented Dialogue Systems

Users of task-oriented dialogue systems are often limited to ‘in-schema queries’, i.e., questions constrained by a predefined database structure. Providing access to additional semi- or unstructured knowledge could enable users to enter a wider range of queries answerable by the system. To this end, we have integrated a Question-Answering (QA)-module in an interactive restaurant search system and evaluated its impact using a crowd-sourced user evaluation. The QA-module includes knowledge selection and response generation components, both driven by fine-tuned GPT-2 language models, and a method to prevent responses unrelated to a user question (‘off-topic responses’). The results show that systems with QA-module are significantly preferred over the baseline without QA-module. Moreover, while the off-topic response prevention method was correctly triggered in 98.1% of questions not covered in the knowledge base, users showed more preference to the system that can retrieve information irrespective of whether it is relevant or not.
Eliciting User Self-disclosure using Reciprocity in Human-Voicebot Conversations

In this study, we have developed a voicebot which asks users questions about their daily activities and social participation to gain insights into their happiness and well-being. We hypothesize that showing disclosure when asking questions can elicit reciprocity of self-disclosure by the users. We define two types of disclosure: self-disclosure and other-disclosure. Self-disclosure is sharing thoughts, feelings and information about oneself, whereas other-disclosure is sharing information about others and opinions of others. We analyzed 122 answers to the voicebot’s disclosure and control questions by annotating the number of self-disclosure statements in the answers. We found no significant effect of asking disclosure questions on the number of self-disclosure statements. However, we did find a positive effect of asking disclosure questions on common markers of reciprocity such as the number of words, topic phrases, and first-person pronouns. Replication of this study with more participants would strengthen the validity of the findings.
Beyond Anthropomorphism: Unraveling the True Priorities of Chatbot Usage in SMEs

This study examined business communication practices with chatbots among various Small and Medium Enterprise (SME) stakeholders in Singapore, including business owners/employees, customers, and developers. Through qualitative interviews and chatbot transcript analysis, we investigated two research questions: (1) How do the expectations of SME stakeholders compare to the conversational design of SME chatbots? and (2) What are the business reasons for SMEs to add human-like features to their chatbots? Our findings revealed that functionality is more crucial than anthropomorphic characteristics, such as personality and name. Stakeholders preferred chatbots that explicitly identified themselves as machines to set appropriate expectations. Customers prioritized efficiency, favoring fixed responses over free text input. Future research should consider the evolving expectations of consumers, business owners, and developers as chatbot technology advances and becomes more widely adopted.
Games Based on Contextual Teaching and Learning: Improving Science Learning Outcomes for Elementary Students

This study aims to: (1) produce learning media that are suitable for use in learning; (2) find out the practicality of the developed media; and (3) find out the effectiveness of CTL-based game learning media.This type of research is development research using the DDD-E development model, namely decide, design, develop, and evaluate.The game media developed consists of materials, match-up games, and quizzes.This research was conducted on fourth grade students at SD N 8 Kebayakan.The design of this study used a posttest only control design, with a sample of 38 students consisting of 19 students as an experimental class who were taught using CTLbased game learning media and 19 students as a control class who were taught with book media, print, and conventional models.The feasibility test obtained a result of 4.42, which means "Very Feasible" and the practicality test obtained a result of 4.57, which means "very practical".The results of the hypothesis test prove that there is a significant difference between the learning outcomes of students who use CTL-based game learning media and the learning outcomes of students who use textbook media.This is indicated by the acquisition of data, namely, a significance value of 0.000.Value (sig) < 0.05, then Ho is rejected and Ha is accepted.So it can be concluded that CTL-based game learning media can improve student learning outcomes in class IV Science subjects at SD N 8 Kebayakan.
The Security War in File Systems: An Empirical Study from A Vulnerability-centric Perspective

This article presents a systematic study on the security of modern file systems, following a vulnerability-centric perspective. Specifically, we collected 377 file system vulnerabilities committed to the CVE database in the past 20 years. We characterize them from four dimensions: why the vulnerabilities appear, how the vulnerabilities can be exploited, what consequences can arise, and how the vulnerabilities are fixed. This way, we build a deep understanding of the attack surfaces faced by file systems, the threats imposed by the attack surfaces, and the good and bad practices in mitigating the attacks in file systems. We envision that our study will bring insights towards the future development of file systems, the enhancement of file system security, and the relevant vulnerability-mitigating solutions.
BAĞLANTI SEVİYESİ SİMÜLASYON ÜZERİNDE LLRNET'İN 5G-NR'YE UYGULANMASI

Modern receivers apply smooth demodulation and demapping processes to received symbols, using bit log-likelihood ratios (LLRs). Known as “LLRnet" demodulator architecture is offered that is a global educatable neural network attributed in this paper. The calculation of the optimal LLR algorithm includes the calculation of each bit in the LLR value and requires the assessment of whole lattice points as high dimensional which is unpractical for the QAM modulation. Known the most in the literature the Maximum Likelihood (ML) detector shows very high computational complexity that is used in the QAM scheme. Besides LLRnet developed achievement importantly, all calculational complexity is also reduced. Via estimating exact log-likelihood, how to create symbols, and channel corruptions for training a neural network LLRNet is shown in this paper. New and contemporary radio communication systems, such as 5G- NR (New Radio) and DVB (Digital Video Broadcasting) for satellite, DVB (S.2 Second Generation) utilize the LLR approach that calculates soft bit values with FEC (Forward Error Correction) algorithms and utilizes demodulated smooth bit values. This article aims a link-level simulation study to implement of LLRnet to DVB S2 and 5G-NR. The motivation of this study is seen that performing machine learning techniques on physical layer scheme, makes LLRNet a powerful example for practicability. This paper offers to compare Max-Log Approximate LLR, Exact LLR, and LLRNet methods for 16, 32 and 128 QAM.
Interpretable Prediction Rules for Congestion Risk in Intensive Care Units

We study the problem of predicting congestion risk in intensive care units (ICUs). Congestion is associated with poor service experience, high costs, and poor health outcomes. By predicting future congestion, decision makers can initiate preventive measures, such as rescheduling activities or increasing short-term capacity, to mitigate the effects of congestion. To this end, we consider well-established queueing models of ICUs and define “high-risk states” as system states that are likely to lead to congestion in the near future. We strive to formulate rules for determining whether a given system state is high risk. We design the rules to be interpretable (informally, easy to understand) for their practical appeal to stakeholders. We show that for simple Markovian queueing systems, such as the [Formula: see text] queue with multiple patient classes, our rules take the form of linear and quadratic functions on the state space. For more general queueing systems, we employ methods from queueing theory, simulation, and machine learning (ML) to devise interpretable prediction rules, and we demonstrate their effectiveness through an extensive computational study, which includes a large-scale ICU model validated using data. Our study shows that congestion risk can be effectively and transparently predicted using linear ML models and interpretable features engineered from the queueing model representation of the system. History: This paper has been accepted for the Service Science/Stochastic Systems Joint Special Issue. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsy.2022.0018 .
Development Of E-Modules For Prayer Practice Using Flip PDF Professional

This study aims to produce electronic prayer practice modules using the Flip PDF Professional application. The research was conducted in the form of research and development (R&amp;D). The development model used is the ADDIE model (analysis, design, development, implementation, and evaluation). The electronic module developed by the author contains material presented using images, videos, audio, and animation to increase students' interest in learning the practice of prayer and effectiveness in the learning process. The results showed that the electronic module developed had met the required standards. Validation was carried out by material experts, media experts, and linguists. In addition, the electronic module has also been field tested on a small scale by 8 learners and a large scale by 15 learners. The interpretation of the material expert shows that this e-module is very feasible with a percentage score of 87.50%, the interpretation of the language expert shows feasibility with a percentage score of 89.58%, and the interpretation of the media expert shows feasibility with a percentage score of 80.27%. The results of the field trial also showed that this e-module received a good assessment with a percentage score of 82.50%. Based on the results of the trial, it can be concluded that the electronic module of prayer practice is included in the good category and received positive responses from experts, students, and teachers. Currently, the implementation of the electronic prayer module in the learning process is being carried out, and the results of this research will be published in the next article Keywords: Electronic Modules, Flip PDF Professional, Prayer, Worship
The Performance Comparison between PoSpace and PoW

The consensus algorithm of Bitcoin, namely proof-of-work (PoW), has been criticized for its high energy consumption and centralization of mining power. PoW needs a lot of processing power to execute cryptographic problems and verify network transactions. In contrast, Chia's consensus mechanism is based on proofs-of-space (PoSpace), which utilizes disk space rather than computing power. PoSpace involves generating a large amount of verifiable storage space on the network and requires less energy consumption than PoW. This paper presents a comparative analysis of the algorithms used by Bitcoin, Ethereum, and Chia. The paper simulates the PoW and PoSpace processes by Java and compares both mechanisms' memory, CPU usage, and time consumption using VisualVM. The experiment results show that PoSpace costs less space and time resources than PoW. The former is more energy-efficient and decentralized.
Challenges, Corresponding Solutions, and Applications of Generative Adversarial Networks

Generative adversarial networks (GANs) have become a popular deep learning framework in the field of artificial intelligence. Researchers have developed various types of GANs, such as Conditional GANs (CGANs), Mode Dropping GANs (MDGANs), and Wasserstein GANs (WGANs), which have been applied to many different fields. Despite the success of GANs in various tasks, there are still some challenges that need to be addressed. For example, model collapse, non-convergence, and diminished gradient could hinder the training of GANs. In this paper, the authors first introduce the basics of GANs before highlighting a few common problems associated with GANs and their corresponding solutions. For instance, they discuss techniques such as mini-batch discrimination and batch normalization that can help mitigate model collapse and diminish gradient problems. Additionally, they cover methods that can improve the convergence rate and stability of GANs, such as using alternative loss functions and incorporating regularization algorithms. Finally, this paper briefly introduces some recent applications of GANs in image generation, video prediction, and other areas. Despite the challenges that still exist, GANs have shown great promise in various fields, and with further research, GANs have the potential to become more robust and efficient models for generating high-quality synthetic data.
Research on property control of intercalated melt-blown nonwoven materials based on correlation analysis and neural network model

Intercalated melt-blown nonwovens can solve the poor compression resilience caused by melt-blown nonwovens fibers, which is helpful to improve the performance of products, so it is of great significance to study the performance control of intercalated melt-blown nonwovens. In this paper, correlation analysis, BP neural network model and canonical correlation analysis model were constructed to analyze the changes of product performance before and after intercution. Finally, the optimal production process parameters were determined as the acceptance distance of 22.98 cm and the hot air speed of 1790 r/min through the construction of the model, which provided a certain theoretical basis for the product performance control mechanism.
Characteristic Analysis and Application of the Optoelectronic Neural Network

In recent years, optoelectronic neural networks have garnered significant attention due to their potential to facilitate high-speed and efficient information processing by employing light-based signals to transmit and process data. This review provides an overview of the current state-of-the-art in optoelectronic neural networks, including their design principles, fabrication techniques, and applications. The article also presents five different methods for constructing optoelectronic neural networks, which offer insights into current ONN research and solutions to overcome the limitations of traditional neural networks. Furthermore, the review discusses three different applications of ONNs, including basic tasks such as data classification, speech recognition, and image recognition, as well as hardware accelerators and SNN algorithms for object detection. The promising potential of optoelectronic neural networks in transforming various fields, such as artificial intelligence, image recognition, and data processing, is also highlighted. As research in this area continues to advance, further breakthroughs in optoelectronic neural networks are anticipated.
The Relationship Between Age, Parity, Ideal Weight, and Blood Pressure in Diagnosing Hypertension in Pregnant Women Using The K-Means Algorithm

Hypertension is one of the health problems that often arise during pregnancy and can cause complications in 2-3% of pregnancies. Hypertension In Pregnancy (HDK) is defined as a blood pressure of ≥140/90 mmHg in two or more measurements. Data mining is a combination of a number of computer science disciplines that is defined as the process of discovering new patterns from very large data sets. By looking at records on Age, IMT, Parity / Gravidity, and Blood Pressure and analysis with K-Means clustering, it can be seen that the similarity of values of the above variables ultimately forms patterns related to hypertension in pregnant women. The clustering process using 5 clusters according to the elbow chart analysis. In this study, it was seen that the variable Blood Pressure is the same pattern and often appears in each cluster. While hypertension occurs in 1 cluster out of 5 existing clusters.
Understand intersection of disability harassment and free speech

Disability harassment is a common problem on campus. However, colleges and universities sometimes have difficulty identifying and addressing such harassment, especially when it involves speech.
A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition

Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. To address this issue, various instance selection (IS) approaches have been proposed, which choose a small fraction of critical instances and screen out others before training. However, existing methods have not been able to balance accuracy and efficiency well. Some methods miss critical instances, while others use complicated selection schemes that require even more execution time than training with all original instances, thus violating the initial intention of IS. In this work, we present a newly developed IS method called Valid Border Recognition (VBR). VBR selects the closest heterogeneous neighbors as valid border instances and incorporates this process into the creation of a reduced Gaussian kernel matrix, thus minimizing the execution time. To improve reliability, we propose a strengthened version of VBR (SVBR). Based on VBR, SVBR gradually adds farther heterogeneous neighbors as complements until the Lagrange multipliers of already selected instances become stable. In numerical experiments, the effectiveness of our proposed methods is verified on benchmark and synthetic datasets in terms of accuracy, execution time and inference time.
Entanglement as the cross-symmetric part of quantum discord

We show that a large class of discord measures can be used to construct entanglement monotones, by proving that the minimal discord over all cross-symmetric state extensions is non-increasing under local operations and classical communications (LOCC). In particular, the result implies that the coherence measure can be used to quantify entanglement since for each coherence measure, the corresponding correlated coherence can be also regarded as a kind of discord. It is well known that the Hilbert-Schmidt distance is not suitable for quantifying entanglement since it is not contractive under quantum operations. At last, the results presented here show that the corresponding entanglement quantifier defined with the Hilbert-Schmidt distance is non-increasing under LOCC, which refutes a long-held but unstated convention that only contractive distances can be used to construct entanglement monotones.
A study on the effect of the CEO's will on the usefulness of the smart factory construction system

Purpose In this study, we will examine whether the firm will of the business manager, the ability to check and prepare for the company's own financial investment, and the knowledge acquisition competence have a significant effect on the usefulness of the smart factory construction system. Methods Hierarchical regression analysis was performed to verify the established hypothesis, and SPSS was used as statistical processing for data empirical analysis. Results The CEO's will had a significant impact on the system usefulness of smart factories, and in particular, it played a mediating role in both the knowledge acquisition competence and corporate preparation between the CEO's will and Smart factory system usefulness. Therefore, in order for the CEO's will to lead to smart factory system usefulness, it means that the company's knowledge acquisition capacity and the company's financial preparation must be followed. Conclusion In order to increase the usefulness of the SME smart factory construction system, it was confirmed that the CEO's will, strengthening of knowledge acquisition capability, and financial preparation of the company are important.
An explorative analysis of current implementation of college hybrid instruction, main issues and support measures

This study analyzed current practice of hybrid instruction and main issues to suggest support measures to further improve college learning. To this end, an online survey of 105 students, as well as class observations and in-depth interviews with college instructors who taught hybrid classes, were conducted. Despite the necessity and convenience of hybrid instruction, instructors and students who experienced hybrid instruction for the first time faced many challenges and difficulties. For instructors, 'increased workload due to on/offline classroom set-up and technical glitches, 'burden of class reconstruction’, ‘facilitating class interaction among on/offline students’ were most difficult to deal with. For students, ‘extra time for hybrid classroom set-up’, ‘difficulty interacting with students in different mode’, ‘technical glitches’ were pointed out as the major issues. In order to facilitate meaningful learning in hybrid instruction, universities should diversify support measures for instructors and, in particular, provide extra support for instructional design customized for hybrid instruction. It is expected that this study can be used as basic data to derive a new teaching-learning model for the operation of college hybrid instruction.
A Performance Comparison of Deep Learning Models for Scene Text Segmentation

Scene text segmentation is a critical task in computer vision with applications in optical character recognition (OCR), document analysis, augmented reality, and content-based image retrieval. It accurately predicts text regions from images, accounting for variations in fonts, sizes, orientations, colors, and backgrounds. Compared to semantic and instance segmentation, however, there are a few dataset and text-specific models. This paper introduces reliable publicly available datasets for scene text segmentation. Additionally, we investigate the text segmentation performances on them with widely-used segmentation models including the text-specific method. Based on the qualitative and quantitative evaluation, this study observes the difficulties of text segmentation and limitations of current models, and discusses solutions.
The Intellectual Structure of Service Robots by Author Co-citation Analysis

Robots are working in both industrial and service sectors. Robots have evolved from one-function automatons to intelligent systems of versatile features, and the new generation of service robots are sharing same space and tasks with humans.BR The purpose of this study is to analyze the intellectual structure of service robots research and to get basic data for policy planning. This study analyzed data from the Web of Science database and we performed clustering analysis, burst detection, and timeline analysis with a total of 7,252 documents and 61,454 references. Author co-citation analysis is used to analyze the intellectual structure of the service robots. As a result of the analysis, total 11 clusters were identified and the top five clusters are “Service robot”, “Concentric tube robot”, “Deep learning”, “Telepresence robot” and “Wearable robot”. And two clusters with high silhouette values were derived as “Magnetic actuation system” and “Aware robot navigation”. We identified the top 4 references with the strongest citation bursts and through timeline analysis we also presented the expecting clusters of the future through timeline analysis.