Reviewerly/exHarmony huggingface dataset:
Reducing Approximation and Estimation Errors with Heterogeneous Annotations

This chapter will present the methods for building natural language processing (NLP) systems using heterogeneous linguistic annotations. Considering that heterogeneous annotations are usually systematically different but highly compatible, we proposed to use them to reduce two main types of errors in statistical NLP, namely, approximation and estimation errors. To support our proposal, we studied Chinese word segmentation and part-of-speech tagging, and then empirically analyzed the diversity between two representative corpora—Chinese Treebank and PKU’s People’s Daily—using the manually mapped data collected. The analysis was further exploited to improve the subword-based lexical processing system by (1) integrating systems that were respectively trained by heterogeneous annotations to reduce approximation errors, and (2) retraining models using high-quality automatically converted data to reduce estimation errors.
Predicate Aggregate Signatures and Applications

Motivated by applications in anonymous reputation systems and blockchain governance, we initiate the study of predicate aggregate signatures (PAS), which is a new primitive that enables users to sign multiple messages, and these individual signatures can be aggregated by a combiner, preserving the anonymity of the signers. The resulting PAS discloses only a brief description of signers for each message and provides assurance that both the signers and their description satisfy the specified public predicate. We formally define PAS and give a construction framework to yield a logarithmic size signature, and further reduce the verification time also to logarithmic. We also give several instantiations for several concrete predicates that may be of independent interest. To showcase its power, we also demonstrate its applications to multiple settings including multi-signatures, aggregate signatures, threshold signatures, (threshold) ring signatures, attribute-based signatures, etc, and advance the state of the art in all of them.
Relationships Between Artificial Intelligence and Emotions in Education

This chapter presents a systematic review on the relationship between artificial intelligence and emotions in education in Latin America and the Caribbean. The PRISMA systematic review methodology was used to describe the state of the situation of research on this topic, taking into account theories, methodologies, countries, and educational levels. Fifteen published articles were finally selected, focusing on Brazil and Colombia, university level, students as unit of analysis, methodologies based on facial recognition, psychology and software combined. It is hoped to deepen the research in other disciplines, with other theories and methodologies.
Definition of the Problem and Junction Conditions

Addressing any question about the technology of gluing may start with any two manifolds with boundaries given they are isometric, or with a given manifold having a singular hypersurface embedded in it, depending on the physical problem we face. In any of these approaches, the isometric feature of the boundary or singular hypersurface is a trivial assumption with some tricky details depending on the timelike/spacelike or lightlike hypersurface which we will discuss here or in Chapter 5, respectively. Once the gluing manifold is assumed to be a solution to our gravity theory, being Einstein or any other generalization or modification to it, we expect other extra gluing conditions coming from dynamics. We will differentiate between isometric requirements, called junction conditions, due to geometry and what we call "gluing conditions" coming from dynamics to remedy misconceptions originating from an interchange of basic geometry and dynamics.
Pelatihan Penggunaan Zahir Accounting Pada Siswa SMK 4 Pekanbaru

This community service was held on Monday, May 29, 2023. This community service was held at SMKN 4 Pekanbaru. There were 34 students who participated in the training, this dedication went smoothly and all participants were able to take part in the training well. In this service activity, participants were given questionnaires before and after the training. The results of the questionnaires before and after the training showed a significant increase in knowledge about Zahir accounting. The school is also very supportive of this community service activity, so that with the support of all parties, the service can run well and be successful.
Multi-Datasets Evaluation of GB-RAS Network Based Stegdetectors Robustness to Domain Adaptation Problem

Protection of state and commercial critical infrastructures is topical task today. Among known threats, special interest is taken to early detection of hidden (steganographic) channels usage by attackers for unauthorized transmission of sensitive data. These channels are created by modification of the innocuous files, such as digital images, for message hiding and further transmission of modified files. Detection of formed stego files is non-trivial task due to the wide usage by attackers of adaptive embedding methods that preserve low level of cover image distortion during embedding. Modern solutions for stego images detection are based on utilization the novel convolution neural networks for revealing weak alterations of cover’s features during message hiding. Training of such networks is performed on fixed set of standard image databases, such as BOSS, ALASKA etc. Despite high detection accuracy of pre-trained stegdetectors, theirs performance “in the wild” on natural images remains an open question. The work is devoted to performance analysis of advanced GB-Ras convolutional network based stegdetector for adaptive embedding methods detection on natural images presented in various datasets. Obtained results proved the effectiveness of neural network applying for mitigation with domain adaptation problem for modern stegdetectors. The GB-Ras model allows improving detection accuracy of stego images for standard ALASKA (up to 6% for for S-UNIWARD embedding methods) and VISION (up to 13% for S-UNIWARD and to 9% for MG embedding methods) datasets in comparison with cover rich models based stegdetectors. Nevertheless, the performance of considered GB-Ras based stegdetector for high quality images from MIRFlickr dataset is conceded to maxSRMd2 rich model based stegdetector (up to 11% for S-UNIWARD and 12% for MG embedding methods). Therefore, considered GB-Ras network may be used as a promising candidate for further design of novel stegdetectors that are robust to domain adaptation problem.
Medical-Radiation-Shielding Film Fabricated by Imitating the Layered Structure Pattern of Abalone Shell and Verification of Its Shielding Effect

Radiation-shielding clothing for medical workers must be light and thin, thus ensuring flexibility. However, controlling the thickness and weight is limited by shielding performance requirements. This study aims to improve shielding performance by considering a shielding structure that mimics the internal structure of an abalone shell. Two shields were produced: a sheet made with a carrier process using a liquid polymer and tungsten mixture, and a fillet made by compounding the same material and laminated using a heat-treatment press after the injection process. The tungsten content and thickness were the same at 85 wt% and 0.3 mm, respectively. In the high-energy region, the shielding film based on the laminated structure of abalone shells showed a shielding rate that was higher by more than 7%. Compared to that of a 0.3 mm lead plate, the shielding ratio of the shielding film was approximately 16% lower at 120 kVp, thereby confirming the radiation-shielding effect of the layered-structure shielding film. Therefore, it is concluded that the laminated structure of the shielding film, which is identical to the internal laminated structure of the abalone shell, expands the impact area of incident radiation and attenuates the energy intensity, thereby improving the medical-radiation-shielding performance.
Determinants affecting customer intention to use chatbots in the banking sector

The study aims to analyze the factors that influence customers’ inclination to utilize chatbots in banking services. The paper employed the technology acceptance model and utilized structural equation modeling to examine the factors affecting consumers’ willingness to embrace chatbot services. The survey evaluated various determinants, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. Data were collected from 250 bank customers in the Bombay region of India through an online survey employing a random sampling method. The collected data were analyzed using IBM SPSS AMOS. This study identifies the aspects of chatbot technology in the banking sector, such as user interface, content, security, and convenience, that influence customers’ decisions to adopt this innovative technology. The results of the analysis revealed path coefficients indicating a significant relationship between information security and perceived usefulness (β = 0.286; p = 0.005) and between perceived usefulness and intention to use (β = 0.489; p &amp;amp;lt; 0.001). Additionally, the path coefficients for design, security, and facilitating conditions were β = 0.281, β = 0.193, and β = 0.136, respectively, all of which held nearly equal significance in the study. The inter-correlations among the variables ranged from 0.346 to 0.854 and were statistically significant. In the banking sector, customers’ intention to use chatbots is influenced by convenience, efficiency, trust, and personalized experiences. Customers are more likely to embrace chatbots when they provide seamless support and tailored solutions, ultimately enhancing customer satisfaction and engagement. AcknowledgmentThis study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445).
Improving students' academic achievement and attitude in science through comprehensive evaluation

Evaluation is to promote, evaluation is to develop. Based on the Student Evaluation Guidelines issued by the Ministry of Education of China as a theoretical framework, this study incorporated classroom performance, extracurricular performance, academic achievement and extended evaluation into the comprehensive evaluation of primary school science learning, and conducted eight years of practical exploration. The data of scientific attitude (classroom performance, extracurricular performance, extended evaluation) and academic performance of 178 students in Grade 3 for a period of 3 years were analyzed. Through multiple comparisons of descriptive statistics, analysis of variance and analysis of variance with Spss26, the research found that: (a) The use of comprehensive evaluation to improve pupils' academic performance in science presents a gradual increasing trend, and there is a significant difference during the school period. (b) Using comprehensive evaluation to improve pupils' attitude towards science learning, showing an overall growth trend. (c) The use of comprehensive evaluation to improve students' classroom performance and expansive evaluation plays a promoting role, showing a gradually increasing trend, and there is a significant difference during the study period. (d) Comprehensive evaluation has a certain impact on the improvement of students' extracurricular performance.
Secured and Sustainable Supply Chain Management Using Key Escrow Encryption Technique in Blockchain Mechanism

INTRODUCTION: Supply chain management is the management process of the flow of goods, and services related to financial functionalities, procurement of raw materials delivery to the final destination.&#x0D; OBJECTIVES: Since the traditional supply chain process lacks data visibility, trustworthiness, and distributed ledger, the need for the blockchain mechanism to ensure the time-stamped transactions to provide a secured supply chain process has been introduced and integrated.&#x0D; METHODS: The distributed nature of the blockchain helps in organizing the supply chain and engaging the customers with real, verifiable, and immutable data. Blockchain technology enables these transactions to be tracked in a very secure and transparent manner. In this paper, we, therefore, propose a framework that utilizes blockchain and key Escrow encryption systems to optimize the security of supply chains to improve services for global business survivability.&#x0D; RESULTS: The comparative analysis with the existing benchmarking techniques with respect to the key size, key generation time, and key distribution time was carried out with the proposed model and found that proposed work provides better results.&#x0D; CONCLUSION: This proposed system can track the authenticity of the product and details about the manufacturer of that particular product. Thus, the paper concludes the proposed work enhances data’s integrity, traceability, and availability and single-point failure can be resolved or reduced using blockchain mechanism.
Electronic Storefront Development Using Responsive Web Design (ACECS)

Food and beverage (F&amp;B) businesses often encounter a common issue: employees struggling to manage customer orders efficiently. Moreover, the lack of a computerized system and online presence can result in the physical location becoming overcrowded during peak hours, causing potential customers to be discouraged by lengthy waiting times. To address the issue, the ordering process needs to be computerized for improved efficiency. This study aims to achieve several objectives: identifying user and system requirements for an ecommerce system in F&amp;B businesses, creating and developing a webbased e-commerce system using responsive web design, and evaluating the system's functionality and usability. To initiate this research, a preliminary analysis of system requirements was conducted through surveys and literature review. The design phase followed, producing essential design diagrams such as the data flow diagram (DFD), which plays a crucial role in shaping system functionalities. The implementation's backend employs the MySQL database for effective data storage and PHP for data processing. On the frontend, Figma was used for initial system design, while Bootstrap, cascading style sheets (CSS), and hypertext markup language (HTML) were used to create a functional website. The research culminated in functionality and usability testing. Usability testing involved expert evaluation with input from two experts. The results acquired give a positive indicator for further implementation. This study sheds light on a tool that could enhance the order process in F&amp;B business outlets. Responsive design features were incorporated to ensure a consistent and user-friendly experience across various devices.
Personalized Gym Trainer Using Mediapipe

Abstract: With the growing demand in personalised fitness experiences, this paper takes a fresh look at home workouts by creating a Personalised Gym Trainer with the Mediapipe library. This sophisticated device combines pose detection technology with voice assistance to provide users with real-time feedback and personalised instruction while exercising. The system identifies various exercises accurately and leverages torso point detection for greater precision by leveraging the capabilities of the Mediapipe library and OpenCV for camera tasks. Individual Python modules for certain workouts such as pull-ups and bench press are written to support a flexible and scalable solution. Python, Git, GitHub, and Jenkins are among the tools and technologies used in the process. Furthermore, the Findpose library is used to calculate angles between torso locations, resulting in Providing a quantitative assessment of proper posture. The suggested Personalised Gym Trainer is a promising leap in home fitness solutions, integrating cutting-edge pose detection with voice assistance for an interactive and personalised workout experience.
Word classes in Eskimo–Aleut languages

Abstract It was once claimed that languages of the Eskimo–Aleut family contain just one lexical category: Nouns. The history of this claim is detailed in Sadock (1999) and further discussed in Woodbury (1985) and Mithun (2009, 2017b), among others. In fact, these languages show robust, distinct Noun and Verb categories (in addition to particles) at both the stem and word levels. Here the criteria that distinguish them are presented, then some subtleties reflecting deeper issues of lexical classification are examined, issues emerging from some major diachronic developments. First, the family is described; second, the features distinguishing Nouns and Verbs in the family are enumerated; third, the issue of flexibility is discussed; fourth, lexical subcategories are surveyed; fifth, the extension of nominalization to higher levels of structure is traced. This last step ultimately explains why it might have once been thought that there was just one lexical category.
The adverse effects of practical passive optical network on continuous-variable quantum key distribution downstream access networks

An quantum access network, connecting multiple end users and a backbone network in a scalable and cost-effective way with tree or star structures, is suitable to combine with continuous-variable quantum key distribution for network security. Under the realistic condition, in a passive optical network in optical distribution network achieved by a practical passive beam splitter, it is hard to divide the signal into 1/n parts evenly. To well quantify the impact of the imperfection, we modify the formula for secret key rate calculation with the imperfection effect and conduct numerical simulations. The results indicate that if the communication parties neglect this imperfection, Eve can attack the entire network and attain information without the parties’ awareness.
Aided Visual Odometry: A Sparse Bundle Adjustment Solution for Rover Navigation

Acquisition of accurate ego-motion estimates sits at the core of autonomous navigational re- search. In this thesis, a method of estimating rover trajectory through stereo visual odometry is proposed. This research presents a navigational system aided by a sun-sensor and inclinome- ter to improve accuracy in motion estimation. Specifically, this research improves on the work conducted by Lambert et al. [1] and investigates the accuracy of previous work as well as the quality of the Devon Island dataset. Sparse implementations of matrices are utilized to effi- ciently determine trajectory estimations using both stereo camera and the secondary attitude sensors. Ultimately this thesis implements an algorithm that results in a significant reduction in computational cost when compared to the work conducted by Lambert et al. [1] iii
Language Models in Automated Essay Scoring: Insights for the Turkish Language

The proliferation of large language models represents a paradigm shift in the landscape of automated essay scoring (AES) systems, fundamentally elevating their accuracy and efficacy. This study presents an extensive examination of large language models, with a particular emphasis on the transformative influence of transformer-based models, such as BERT, mBERT, LaBSE, and GPT, in augmenting the accuracy of multilingual AES systems. The exploration of these advancements within the context of the Turkish language serves as a compelling illustration of the potential for harnessing large language models to elevate AES performance in in low-resource linguistic environments. Our study provides valuable insights for the ongoing discourse on the intersection of artificial intelligence and educational assessment.
Digital Interactive Learning Method and its Influence on College Art Students’ Learning Satisfaction

This study evaluated the effectiveness of digital interactive learning methods in art classes, and the learning satisfaction of college students in Xinyang Normal University (XNU). The study used a descriptive-comparative correlational design and the respondents of the study are 100 students from Xinyang Normal University (XNU). The use of digital interactive learning methods was highly effective according to the teacher respondents. Both students and teachers are highly satisfied in their learning with the use of digital interactive learning methods. The assessment of the effectiveness of digital interactive learning methods does not vary significantly across different levels of sex, age, and year level. Moreover, there is a significant difference in the assessments of both respondents on the effectiveness of virtual learning and overall digital interactive learning method, there is a significant difference in the satisfaction of assessment of the students among different year levels, and the fourth-year students have a significantly higher satisfaction compared to third year students. Furthermore, there is a significant direct relationship between the assessment of video-based learning effectiveness and student satisfaction with generic skills and learning experiences. The assessment of digital interactive learning effectiveness is significantly correlated with overall satisfaction. Additionally, there are direct relationships between game-based learning and satisfaction with teaching, as well as digital interactive learning method and satisfaction with generic skills and learning experiences. However, no significant relationships were observed between other variables.
A Categorical Metamodel for Reactive Kripke Frames

Abstract In a reactive Kripke model, evaluation of logical operators can cause reconfiguration of the model in which the formula is evaluated. Therefore, in reactive Kripke frames, the evaluation of a logical formula in a world depends both on the world in which the evaluation is made and on the worlds it has passed through previously. The result is an extended semantics, which can specify a class of modal logics more comprehensive than the class specified by ordinary Kripke frames. This paper introduces a metamodel for reactive Kripke frames, based on the concept of categorical sketch. We believe that the categorical sketch is an appropriate metamodel for specifying a Kripke frame model.
Emotional Analysis of Customer Reviews Using Natural Language Processing

We know that technology has been transforming very fast these days. How much technology is ahead? Are there going to be some problems with the technology we created? The problemsdiscussed here are about the way the customer's product review emotions are expressed. We are finding out the person's emotional state. By doing this analysis, the approach towards the customer is going to be particular. The approach is emotion based. This emotion-based approach makes a good impression on the organization. This analysis gives us an easier hand on the review analysis of the products sold. Review analysis techniques are widely used by many multinational companies.
Improvement the sense of being listened to by using a video conferencing system to visualize focal words of user’s speaking in a thought bubble

In recent web conference systems, it has become possible to express the user’s gaze and attention through image processing. However, many users feel uncomfortable turning on the camera, and the sense of looked at and listened to worsens when the camera is turned off. Therefore, in this study, we investigated whether it is possible to improve the sense of gaze and listening by using thought bubbles even when the camera is off and there is no face information of the interlocutor. We have developed a video conferencing system that displays a pictogram image of a person with a thought bubble. The thought bubble displays the user’s face image. In addition, it is possible to extract the words that are the focal points of the user’s utterances and display them in a thought bubble. As a result, it was confirmed that the interlocutor’s sense of being looked at was enhanced by the presence of a thought bubble even when the interlocutor shows pictogram images of people. This result also suggests that thought bubbles can effectively show an interlocutor’s attention.