Reviewerly/exHarmony huggingface dataset:
Design of Teaching Mode for the Course "Introduction to Database Systems" in the Network Environment

In the current context of new engineering, by optimizing and improving the existing teaching content and methods, the teaching effectiveness and quality of the database system overview course can be comprehensively improved, thereby breaking the traditional course content system and building a complete and comprehensive teaching platform on the basis of the original teaching.By utilizing various teaching resources reasonably, students' comprehensive and professional literacy can be comprehensively cultivated.This article conducts research and analysis on the teaching mode design of the course "Introduction to Database Systems".
Synthesizing Speech Test Cases with Text-to-Speech? An Empirical Study on the False Alarms in Automated Speech Recognition Testing

Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms. Given a failed test case synthesised from TTS systems, which consists of TTS-generated audio and the corresponding ground truth text, we feed the human audio stating the same text to an ASR system. If human audio can be correctly transcribed, an instance of a false alarm is detected.
Current Status and Issues of GVC in Korea’s Major industries: Focusing on the Electronics and Automobiles Industries

The recent GVC disconnection caused by the Russia-Ukraine war, COVID-19, US-China hegemony and technology competition requires strategic policy establishment and preparation at the national level as it goes beyond firm-level response capabilities. Since transactions under already established supply chains still account for a significant portion of trade in the perspective of firms, based on diagnosis and objective understanding of the current status, efforts are required to actively respond to changes in the global economic-security and industrial environment. Therefore, this study analyzes the current status of GVC, focusing on the electronics and automobile industries, which are the main industries that contribute the most to Korea's domestic value-added exports, and summarizes the implications related to GVC restructuring to respond to the changing trade environment.&#x0D; This study uses ICIO (2021), the most up-to-date data as of 2023, to analyze the results of value-added export decomposition of Korea's main export industries, electronics and automobiles. In particular, the study is meaningful in that it links recent changes centered on the US-China in the macro policy environment (country-industry) to the implications of the microscopic aspect (industry-item).
Loss-tolerant BB84 Quantum Key Distributions with State Preparation and Measurement Imperfections

The source and detection modules serve as vital components of quantum key distribution (QKD) systems. Nonetheless, in practical applications, the efficiency of QKD systems is hindered by source flaws and detector imperfections. In this paper, based on the loss-tolerant method, we consider the impact of state preparation flaws and detection defects (afterpulse, dead-time) on BB84 QKD systems. Our simulation results show that with the the state preparation flaw, afterpulse probability and dead-time increase, although the key rate and the transmission distance reduces, the loss-tolerant BB84 QKD system is much more robust and show promising behaviors in practical implementations.
Research on clock holding technology based on PSO-BP neural network

Aiming at the problem that the local crystal oscillator is affected by its own aging factors, which will lead to a decrease in the retention accuracy of the clock synchronization system, firstly, by introducing the particle swarm optimization algorithm, the selection of the initial weight and threshold of the BP neural network is optimized, and the convergence speed is improved. Then use the PSO-BP neural network model to fit and predict the aging data of the two groups of crystal oscillators, establish a related aging model, compare the prediction error of the BP neural network model before and after the optimization of the particle swarm algorithm, and verify the good optimization ability of the particle swarm algorithm. Finally, the model is applied to the clock synchronization system. The frequency accuracy of the system within 24 hours of reference 1PPS signal loss can be maintained at the order of $\pm 8 \times 10^{-11}$, achieving a high-precision clock retention effect.
Device's Operation Tracking using Blockchain in Industrial Control System

Many producing, monitoring and controlling needs are met by using programmable logic controllers.But there is no effective mechanism to audit PLC behavior.So this research designed a mechanism based on Blockchain for the purpose of effectively recording the commands and response actions received by the PLC.Due to the characteristics of the blockchain, the integrity of the data is also guaranteed..
A generalization of de Vries duality to closed relations between compact Hausdorff spaces

Stone duality generalizes to an equivalence between the categories StoneR of Stone spaces and closed relations and BAS of boolean algebras and subordination relations. Splitting equivalences in StoneR yields a category that is equivalent to the category KHausR of compact Hausdorff spaces and closed relations. Similarly, splitting equivalences in BAS yields a category that is equivalent to the category DeVS of de Vries algebras and compatible subordination relations. Applying the machinery of allegories then yields that KHausR is equivalent to DeVS, thus resolving a problem recently raised in the literature. The equivalence between KHausR and DeVS further restricts to an equivalence between the category KHaus of compact Hausdorff spaces and continuous functions and the wide subcategory DeVF of DeVS whose morphisms satisfy additional conditions. This yields an alternative to de Vries duality. One advantage of this approach is that composition of morphisms is usual relation composition.
Cascading Blend Network for Image Inpainting

Image inpainting refers to filling in unknown regions with known knowledge, which is in full flourish accompanied by the popularity and prosperity of deep convolutional networks. Current inpainting methods have excelled in completing small-sized corruption or specifically masked images. However, for large-proportion corrupted images, most attention-based and structure-based approaches, though reported with state-of-the-art performance, fail to reconstruct high-quality results due to the short consideration of semantic relevance. To relieve the above problem, in this paper, we propose a novel image inpainting approach, namely cascading blend network (CBNet), to strengthen the capacity of feature representation. As a whole, we introduce an adjacent transfer attention (ATA) module in the decoder, which preserves contour structure reasonably from the deep layer and blends structure-texture information from the shadow layer. In a coarse to delicate manner, a multi-scale contextual blend (MCB) block is further designed to felicitously assemble the multi-stage feature information. In addition, to ensure a high qualified hybrid of the feature information, extra deep supervision is applied to the intermediate features through a cascaded loss. Qualitative and quantitative experiments on the Paris StreetView, CelebA, and Places2 datasets demonstrate the superior performance of our approach compared with most state-of-the-art algorithms.
Does this Explanation Help? Designing Local Model-agnostic Explanation Representations and an Experimental Evaluation Using Eye-tracking Technology

In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However, the user perspective has received less attention in XAI research, leading to a (1) lack of involvement of users in the design process of local model-agnostic explanations representations and (2) a limited understanding of how users visually attend them. Against this backdrop, we refined representations of local explanations from four well-established model-agnostic XAI methods in an iterative design process with users. Moreover, we evaluated the refined explanation representations in a laboratory experiment using eye-tracking technology as well as self-reports and interviews. Our results show that users do not necessarily prefer simple explanations and that their individual characteristics, such as gender and previous experience with AI systems, strongly influence their preferences. In addition, users find that some explanations are only useful in certain scenarios making the selection of an appropriate explanation highly dependent on context. With our work, we contribute to ongoing research to improve transparency in AI.
Natalia Knoblock (ed.), The grammar of hate: Morphosyntactic features of hateful, aggressive, and dehumanizing discourse. Cambridge: Cambridge University Press, 2022. Pp. xvi + 293.

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Should Your Chatbot Joke? Driving Conversion Through the Humour of a Chatbot Greeting

Despite the increasing number of companies employing chatbots for tasks that previously needed human involvement, researchers and managers are only now beginning to examine chatbots in customer-brand relationship-building efforts. Not much is known, however, about how managers could modify their chatbot greeting, especially incorporating humour, to increase engagement and foster positive customer–brand interactions. The research aims to investigate how humour in a chatbot welcome message influences customers’ emotional attachment and conversion-to-lead through the mediating role of engagement. The findings of the experiment indicate that conversion-to-lead and emotional attachment rise when chatbots begin with a humorous (vs neutral) greeting. Engagement mediates this effect such that a humorous (vs neutral) greeting sparks engagement and thus makes users more emotionally attached and willing to give out their contact information to the brand.&#x0D; The study contributes to the existing research on chatbots, combining and expanding previous research on human–computer interaction and, more specifically, human–chatbot interaction, as well as the usage of humour in conversational marketing contexts. This study provides managers with insight into how chatbot greetings can engage consumers and convert them into leads.
Athlete body fat rate monitoring and motion image simulation based on SDN data center network and sensors

With the development of artificial intelligence technology, new software is also emerging in an endless stream. On the basis of sensors, the new software realizes the separation of network control layer and data layer, thereby improving network throughput and link utilization. With the gradual maturity of deep reinforcement learning technology, the redefined network architecture can be managed and controlled through software, making the network evolve toward a more intelligent direction. By providing data support for the intelligent control of the network, the network controller can obtain the data transmission status in real time, so that more ideas can become reality. Now, on the basis of motion sensors, through data fusion technology, athletes' physical conditions can be planned more effectively, so as to achieve scientific management and reasonable planning, obtain more accurate body fat rates, and customize corresponding data flow routing strategies., To achieve the combination of technology and technology. This paper proposes a scheduling strategy based on machine learning, combining the reinforcement learning algorithm in machine learning and deep reinforcement learning algorithm, setting the key factors of reinforcement learning, and applying it to real-time sports images of athletes, combining the sports characteristics of athletes, Set the action and reward value. Then use the algorithm to allocate a reasonable path for data transmission according to the real-time status to reduce network delay. This article will use sensor technology and data center network to provide a new method for athletes' real-time motion images and body fat percentage.
AVANÇOS E PERCEPÇÕES SOBRE A ATUAÇÃO DA ENFERMAGEM NO CAMPO DA ESTÉTICA

Introduction: Health professionals as nurses by virtue of their aesthetic behavior become valuable people because, in addition to "traditional" interventions, they contribute to the general aesthetic health process with new paradigms and provide patients with more autonomy and expansion. Objective: To evidence the advances and perceptions about the performance of nurses in the performance of advanced aesthetic procedures. Methodology: Scoping Review, based on the procedures recommended by the Joanna Briggs Institute. The guiding question was established: "What paths can nursing take to rise in the field of aesthetics?" Searches were conducted in four national and international databases on works published up to December 2022. Of the 138 studies found, 20 were selected for full reading, resulting in a final sample of 08 studies analyzed. Results: Ten studies that met the eligibility criteria were selected for this review. The articles showed that nursing also has the capacity to expand its field of action to the aesthetic area and seeks recognition in this niche in its space. In addition, the prospects for acting in the field of aesthetics have shown positive trends for aesthete nurses to develop their own networks of professionals, while these professionals have gained more autonomy to develop care practices. The 08 publications analyzed were published from 2018 to 2022, nationally and internationally with experimental study designs. Conclusion: It is concluded that for nurses the insertion in the field of aesthetics is an opportunity for financial and intellectual complementarity, technical improvement and job satisfaction.
Prediction of Face Emotion with Labelled Selective Transfer Machine as a Generalized Emotion Classifier

Emotion prediction and classification using facial expressions still remains as the utmost thought-provoking activity in the computing arena. The most traditional approaches largely depend on pre-processing and feature extraction techniques. This paper factually represents the implementation and evaluation of learning algorithms like SVM, Random Forest, KNN, CNN and LSTM for recognition and predicting emotions of 2D facial expressions based on recognition rate as well as effect of unbalanced datasets. The projected structure in this paper has targeted two datasets, CK+ and JAFFE for two different feature extraction and classification techniques like supervised and unsupervised techniques. Fusion of histogram equalization, PCA and LBP are used for pre-processing. LSTM technique which is normally used with textual or vocal is used for 2D images which is also evaluated with a self modelled CNN algorithm. The recognition rate with LSTM was 76.59% and 98.43% respectively on the CK+ and JAFFE datasets. CNN also showed 70.58% of recognition rate on JAFFE where as 71.56% on CK+ dataset.
Video Tampering Detection Using Machine Learning and Deep Learning

Digital recordings are now affordable, quick to make, and straightforward to upload on web platforms because of the widespread availability of video recording technology in smartphones and other digital devices. The truth of films published on social media cannot be trusted. With the latest tools used for video editing tools, movies may now be readily changed (faked) for political ends or unlawful profit. Over the years, a lot of effort has gone into creating novel methods for identifying various forms of video manipulation. The objectives of video tampering detection include identifying signs of change and assessing the validity and integrity of the video file. These methods are divided into two groups: active & passive (blind) techniques. Based on the forgeries that passive video tampering detection systems can see, they may be divided into three groups: region tampering detection, double or multiple compressed video detection, and video inter-frame forgery detection. The previous studies under consideration are provided in a condensed tabular format, together with the criteria they employed and any limitations they had. The research work concluded by outlining a number of problems that might be used to inspire new research topics for the identification of passive video manipulation. In this paper, few AI/ML algorithms were tested for video tampering detection to find out which one gives out the most accurate results.
On the Performance of Dirichlet Prior Mixture of Generalized Linear Mixed Models for Zero Truncated Count Data

In this study, the performance of Dirichlet Process Mixture of Generalized Linear Mixed Models (DPMGLMMs) was examined against some competing models for fitting zero-truncated count data.The Bayesian models such as Monte Carlo Markov Chain GLMMs, Bayesian Discrete Weibull and the frequentists models such as Zero truncated Poisson, Zero truncated Binomial and Zero truncated Geometric models were compared with the proposed DPMGLMMs model.Simulation and life count data from health domain was used to compare the performance of DPMGLMM with the Bayesian and frequentist models considered in this study.The results showed that the DPMGLMM outperformed other models considered for fitting count data that is truncated at zero.
The impact of a teacher’s professional identity on his/her occupational happiness: The mediating role of psychological capital

Purpose This paper is to explore the factors that influence the professional identity of university teachers on their occupational happiness, and to understand and study the mediating role of psychological capital between a teacher’s professional identity and a teacher’s occupational happiness. Design/Methodology/Approach: This paper uses a quantitative approach to testing. And this paper randomly selected 209 teachers (including full-time teachers and managers) from the Hebei Academy of Fine Arts located in Shijiazhuang, Hebei Province, as the research subjects. Findings: There exists a positive correlation between a teacher's professional identity and their occupational happiness. Additionally, a positive association can be observed between a teacher's professional individuality and their psychological capital. Furthermore, a positive relationship can be identified between a teacher's psychological capital and their professional happiness. Psychological capital plays a mediating role between a teacher’s professional identity and occupational happiness. Conclusion: Professional happiness is a manifestation of happiness at work, and psychological capital and professional happiness have the same theoretical basis and research orientation. The higher their sense of professional identity, the better their attitude towards work, and the higher their sense of professional happiness. Research Limitations/Implications: The sampling only comes from the Hebei Academy of Fine Arts located in Shijiazhuang, Hebei Province, China. The conclusion may not fit all levels of education and teachers' situations. Practical Implications: Universities could focus on improving the teacher's professional identity to improve their occupational Happiness, and universities could train teachers to gain psychological capital to help them keep a positive attitude towards their job to improve their occupational Happiness. Contribution to Literature: This study provides a structural framework for the relationship between psychological capital and occupational happiness that future researchers may use in their studies. The subsequent research could examine this model's applicability to the professional satisfaction of high school teachers or primary school teachers.
Development of Poetry Writing Textbooks in Indonesian Language Subjects for Junior High School Students

This research includes research and development (Research and Development), which aims to describe the development of poetry writing textbooks in Indonesian subjects for junior high school students. The subjects in this study were students and class teachers at VIII SMP Muhammadiyah 6 Dau Malang. The development model used in this study is the R2D2 development model (Recursive, Reflective Design and Development model), which consists of three stages: (1) defining, (2) designing and developing, and (3) disseminating. The development of poetry writing textbooks on Indonesian subjects has gone through a trial process consisting of individual trials, small group trials, and class trials. Textbooks for writing poetry on Indonesian subjects developed have been validated by validators and have undergone revisions so as to get very feasible and practical results for use in learning. The development of poetry writing textbooks in Indonesian subjects consists of (1) covers, (2) book usage guidelines, (3) Pancasila student strengthening profiles (P4), (4) activities, (5) literacy skills, (6) new vocabulary, (7) perception, (8) material, (9) audio features, (10) video features, (11) game features, (12) diagnostic assessment features, (13) diversity of question forms, (14) hots questions, and (15) enrichment features. The fifteen points developed have met the criteria and have a very valid level, so they are very feasible and practical to use in the Indonesian learning process.
ITEM RECOMMENDATION SYSTEM USING MACHINE LEARNING

Every business and organisation today is focused on both meeting client expectations and managing inventories as we achieve new heights of development. By comprehending the pattern from previous sales records, the models they apply enable them to forecast future demand. Everyone has recently given up on using the conventional sales forecasting prediction algorithms since they take so long to produce the desired outcomes. As a result, retailers now keep track of their sales history using a data set that includes information on price tag, outlet type, outlet location, item visibility, item outlet sales, etc. Keywords: Prediction, Regression, Machine Learning, XG Boost, Big Mart, Data Science.
Development and Acceptability of E-Tech Mobile Application

Educational leaders made the decision to adopt the new normal in education in response to the widespread COVID-19 pandemic. The goal of the study was to create an E-Tech mobile application and assess its acceptability using standards specified in ISO 25010: 2011. This study utilized the developmental method of research. The data needed were gathered with the use of an evaluation sheet to evaluate the level of acceptability of the mobile application in terms of functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. The E-Tech mobile application is a learner-centered app intended to deliver the services of DepEd to its clients anytime and anywhere. The users of the product served as the respondents of the study. Based on the results, the registration, log-in and log-out, viewing module, download/storage, assignment, notes, and learners list features are all present and functioning features of the mobile application. Furthermore, when all the 22 expert respondents and 20 ICT teachers were taken as a whole group, they evaluated the level of acceptability of the mobile application as very acceptable. Likewise, they evaluated the level of acceptability of the E-Tech mobile application as very acceptable in terms of functional suitability, performance efficiency, reliability, usability, compatibility, security, maintainability, and portability.