Reviewerly/exHarmony huggingface dataset:
Peran Kepemimpinan Transformasional Kepala Sekolah Dalam Progam Gerakan Literasi Sekolah di SMA Negeri 1 Cibungbulang

The school literacy movement (GLS) is an effort made by educational institutions related to increasing the awareness of students to read and write so that they will gain broader knowledge and insights. The purpose of this study was to describe the influence of the school principal's transformational leadership on the school literacy movement at SMA Negeri 1 Cibungbulang, Bogor Regency. The research method used is a type of literature study research. Data collection in this study was obtained by examining and then linking reading sources to problems in the research area with the transformational leadership contributions of school principals to the school literacy movement. The principal of SMA Negeri 1 Cibungbulang has strategies and efforts to support the GLS program, namely making literacy journals, procuring class libraries, geulis trees, class bulletin boards, and literacy ambassadors. The GLS program that is being implemented is able to motivate students to increase their interest in reading, to be able to make good use of time, especially with the Geulis tree program and class madding. Besides reading, students can imagine and be creative in expressing their ideas from something they read. This is proven to be able to eliminate boredom during literacy hours.&#x0D; Keywords: School Literacy Movement, Transformational Leadership
Application analysis of data mining based on improved decision tree in English flipped classroom teaching

This study aims to analyse the application of data mining based on improve decision in English flipped classroom teaching. The experimental results show that the improved C4.5 algorithm had a better performance than ID3 algorithm and C4.5 algorithm and CART algorithm. In terms of accuracy rate, the four algorithms can be ranked as improved C4.5 algorithm, C4.5 algorithm, ID3 algorithm, and CART algorithm from high to low. Moreover, the improved C4.5 algorithm had the lowest error rate among the four algorithms. Under the same number of training samples, the improved C4.5 algorithm takes the least time at the least memory cost. Therefore, the improved C4.5 algorithm is adopted to construct a data mining model for English flipped classroom teaching and promote the research on English flipped classroom teaching.
Sim-to-Real Transfer for Object Detection in Aerial Inspections of Transmission Towers

Training deep learning models for object detection usually requires a large amount of data, a condition that is not common for most real-world applications, especially in the context of aerial imagery. One possible solution is the use of simulators to generate synthetic data. For a good generalization, the model must be able to learn on the simulated data and perform correctly on the real data, process known as sim-to-real transfer. In this work, we analyze the generation of synthetic data to account for a data-scarce real-world scenario, which includes aerial imagery and object detection of transmission towers and their components. We evaluate the impact of image-to-image translation methods as domain adaptation techniques. In this analysis, we explore training strategies to mitigate the domain shift between synthetic and real data. According to our experimental results, the use of domain-adapted data through image-to-image translation could slightly improve the detection performance in real test data when compared to training with raw synthetic images only or with small datasets of real data, although it was noted through a visual analysis that objects with small bounding boxes, like clamp, anchoring clamp and ball link, could be distorted or vanished by the application of image-to-image translation methods. Additionally, when only a small subset of real data is available, training with both real and synthetic data at once led to better detection results, surpassing combinations of pre-training on synthetic and fine-tuning on real data.
A Computational Approach to Object Classification

This paper describes a computational approach to object classification.This paper introduces the reasoning behind pursuing integrals as a mathematical foundation for object detection.In addition this paper outlines the potential steps in an algorithm that can detect objects.The key advantage of this algorithm is its computational complexity.The algorithm can be simplified to a series of subtractions which are O(1) operations versus the much more computationally complex convolutional neural network approach typically applied to classify objects.
Modeling Morality-Based Argumentation for Believable Game Characters: A Design Postmortem

An ability to morally reason is crucial to the believability of many fictional characters, from Jane Austen’s heroines to the denizens of The Good Place. These works often foreground the complexity of moral questions and the circumstances un- der which different forms of behavior might be justified. Morality is also foregrounded in many games, from Black and White to Mass Effect 3. Yet, most in-game characters judge other characters (or the player) based on a single reputation scale or binary values of right and wrong. There has been little exploration in games of the relationship between char- acter values and beliefs and moral reasoning. In keeping with this year’s conference theme, “Oh the Humanity,” this design postmortem paper describes the design and development of Argument Box, a model of moral argumentation and reason- ing based on Lakoff’s metaphor theory of moral politics. We describe our design approach, iterations, and authoring con- cerns — covering what went right and wrong in our attempts to model morality-based argumentation for believable game characters.
Physics-Based Task Generation through Causal Sequence of Physical Interactions

Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.
There and Back Again: Extracting Formal Domains for Controllable Neurosymbolic Story Authoring

Story generators using language models offer the automatic production of highly fluent narrative content, but they are hard to control and understand, seizing creative tasks that many authors wish to perform themselves. On the other hand, planning-based story generators are highly controllable and easily understood but require story domains that must be laboriously crafted; further, they lack the capacity for fluent language generation. In this paper, we explore hybrid approaches that aim to bridge the gap between language models and narrative planners. First, we demonstrate that language models can be used to author narrative planning domains from natural language stories with minimal human intervention. Second, we explore the reverse, demonstrating that we can use logical story domains and plans to produce stories that respect the narrative commitments of the planner. In doing so, we aim to build a foundation for human-centric authoring tools that facilitate novel creative experiences.
An Opinion Mining-Based Pretrained Model Analysis Depending on Multiple Thinking Patterns

People love to express their opinions online, and the use of social media for this purpose has skyrocketed in the Internet age. Customers also frequently share their first-hand knowledge of the goods or services they have used. Reviews can be either favorable or bad, and both have an impact on people's decisions and businesses. Therefore, it is essential to predict people's opinions in order to preserve and understand the reliability of online review systems. Our study concentrates on a certain kind of model, namely an opinion mining-based pre-trained model, and its analysis takes different types of thought patterns into account. Pretrained models based on transformers, such as BERT, RoBERTa, and DistilBERT may be effective at classifying people's opinions. Other classifiers, such as DISTILBERT and RoBERTa, offer more accuracy compared to the rest of the classifiers employed by researchers but in our model, RoBERTa offers the best accuracy at 90%.
Application of Design Thinking Method in Educational Card Design "Sobat Saleh"

: In developmental psychology theory, students are late adolescents who need to have problem-solving skills (Sahrah, 2019). Itis necessary to be trained in order to become a capable, creative and independent person in accordance with the goals of Indonesian national education. However, in the learning process in the studio class, the DKV study program which has the purpose of learning to hone students' thinking skills, often has difficulty solving a problem. A study that has been conducted previously that it is known that aspects of methods in solving problems are still confusing for students (Ulita &amp;; Hananto, 2022). The research outlines that some students are still not fully aware of how to apply the method in designing. Therefore, this journal will describe the form of application of design thinking methods in a case study of designing an educational card game "Sobat Saleh" that can help student design learning that provides an overview of the process of solving problems into a design idea. Starting from data collection, analytical techniques and thinking processes as well as the form of application of the design thinking design method. Thus, it can be observed more deeply the patterns carried out in efforts to apply the design thinking method so as to help students more easily have the ability to solve problems with the method. The ability of critical thinking and creative things in solving problems can support the design logic that is needed by a designer when doing a design.
A PERFORMANCE COMPARISON OF SORTING ALGORITHMS IN DIFFERENT PROGRAMMING LANGUAGES

Sorting, as one of the basic algorithms, has a wide range of applications in software development. As the amount of processed data grows, the need for fast and efficient data sorting increases significantly. There are many sorting algorithms and their extensions. However, choosing the best and most versatile among them is impossible. All these algorithms have their specifics, which determine the scope of their effective use. Therefore, the problem of deciding the optimal algorithm for certain specific conditions is relevant. This choice is often a non-trivial task, and an unsuccessful choice of algorithm can cause difficulties with data processing performance. To determine which algorithm will be the best in a particular situation, you need to analyse all the factors that affect the operation of algorithms: the size and structure of the data set, the range of element values, the form of access (random or sequential), the orderliness, the amount of additional memory required to execute the algorithm, etc. In addition, different algorithms have different performance in different programming languages. The study analyses the advantages and disadvantages of nine popular sorting algorithms (Bubble, Insertion, Selection, Shell, Merge, Quick, Counting, Radix, and Heap) due to their specifics and limitations on their possible use. The performance of these algorithms implemented in four popular programming languages (C++, C#, Java and JavaScript) is tested. We experimentally discovered that the performance of sorting algorithms differs depending on the programming language. The applied aspect of the study is that its conclusions and results will allow developers to choose the best algorithm for a particular programming language, depending on the size, range, structure, etc. of the data set to be sorted. Considering this is significant when we have to sort large amounts of data in search engines, scientific and engineering applications. After all, the sorting algorithm's efficiency significantly affects the system's overall performance.
Academic Performance Assessment Prediction Model Using the Adaptive Neuro-Fuzzy Inference System Method

Performance evaluation of Human Resources is an important part of an organization or company. One of the HR in tertiary institutions that must be evaluated is academic performance. The author conducted research on the factors of academic performance assessment. Therefore, we need a system that can classify academic performance optimally in order to improve the quality of academic performance. In this research, we construct a predictive model for academic ability evaluation using Adaptive Neuro-Fuzzy Inference System method. The ANFIS method shows a very good data accuracy of 92.20%. From each variable motivation to work appraisal show is a Good, competence to work appraisal = Good, compensation to work appraisal show is a Good, responsibility to work appraisal show is a Good, and job satisfaction to work assessment show is Good.
“The Best Data Are Fake Data?”: An Interview With Chris Hazard

In this issue, we interview Dr. Chris Hazard, cofounder of Diveplane, which is a leader in the burgeoning international synthetic data market. Dr. Hazard discusses the ethical implications of using synthetic data generated from real information sources.
A Payment Channel Network Fee Allocation Strategy Integrating Auction Theory

The payment channel network is an auspicious approach to improve the blockchain scalability, by allowing multi-hop off-chain payments between any two nodes. Therefore, a good incentive mechanism is very important to encourage nodes to actively assist multi-hop payments. However, the current fee allocation strategies do not allocate the fee reasonably and lacks the actual incentive attribute.In this paper, we introduce the auction theory, and propose the second-price dynamic auction fee allocation strategy. This strategy includes node pricing and fee allocation. Specially, the pricing of nodes is determined by six parts: base fee, proportional fee, skew fee, density fee, time fee and other fees. The nodes in the path obtain fees according to its own bid and the lowest bid and the second lowest bid in the auction. Additionally, we find that there is an incentive explosion problem in the fee allocation strategy determined by intermediate nodes, and proposes a virtual fee mechanism to alleviate this problem. The simulation results show that our second-price dynamic auction fee allocation strategy has more fair allocation performance than the Lightning Network fee allocation strategy, and has the ability to resist incentive explosion.
Securing the Internet of Things Network using a Hierarchical Hyperledger Fabric Model

Smart contracts in the Blockchain were initially applied in the financial services domain and later, incorporated into the Internet of Things (IoT) networks because the Blockchain can complement the IoT networks by boosting the benefits through the modularity of the network and peer-to-peer connections. In this paper, we aim to address the issue that prevents the widespread adoption of Blockchain technology in the IoT network, and more specifically, we want to address the case where there is no need for all the IoT devices to communicate with each other. Such cases can be related to smart grids, smart homes and smart cities, and also, smart vehicles to a lesser extent. All of these cases mostly have a central authority, which requires a means to communicate with its clients securely. Our proposed solution makes use of a hierarchical approach to the Blockchain on the IoT network so that we can control the size of the ledger that each sub-network would require. Our solution makes use of Hyperledger Fabric, which is a free and open-source Blockchain framework and helps resolve the ledger size, and cost issues that plague many of the existing solutions in the literature without compromising on the main benefits of the Blockchain. As such, the proposed model enhances the integrity, authorization, and authentication of the communication between the IoT devices in the network. With this in mind, the metaverse requires IoT devices like sensors to interact with the real world. Hence the need to securely manage the sensor swarm is of high importance.
akaTick: Hybrid Mobile E-Ticketing System Based on Non-Fungible Tokens

We propose a hybrid system to utilize non-fungible tokens for event ticketing. By adding an intermediate database, we enjoy blockchain benefits such as fraud prevention and higher transparency over the secondary market while ensuring reasonable redemption speed. Furthermore, the additional step after ticket redemption is to verify through the smart contract whether the ticket holder’s signature is included, preventing event organizers from marking the ticket as used without the holder’s consent.
A Systematic Exploration of Edge Computing-Enabled Metaverse

Metaverse is a concept that aims to create a virtual-reality space where users can engage in social and communication activities. This virtual world is expected to be enabled by a number of key technologies, including edge computing. However, the specific details of how edge computing can boost the development of metaverse still require further investigation, as current literature does not provide sufficient explanations. In this article, we aim to systematically explore the impact of edge computing on the development of metaverse. Firstly, we provide an overview of the architectural design of a metaverse that incorporates edge computing. Next, we delve into the role of edge computing in the metaverse by addressing questions such as why and how it can enhance the virtual world. A use case is then presented to demonstrate how metaverse tasks are offloaded and executed in edge computing-enabled metaverse. Finally, we address some of the challenges associated with this field and outline potential directions for future research.
Cross-Scale Fusion Transformer for Histopathological Image Classification

Histopathological images provide the medical evidences to help the disease diagnosis. However, pathologists are not always available or are overloaded by work. Moreover, the variations of pathological images with respect to different organs, cell sizes and magnification factors lead to the difficulty of developing a general method to solve the histopathological image classification problems. To address these issues, we propose a novel cross-scale fusion (CSF) transformer which consists of the multiple field-of-view patch embedding module, the transformer encoders and the cross-fusion modules. Based on the proposed modules, the CSF transformer can effectively integrate patch embeddings of different field-of-views to learn cross-scale contextual correlations, which represent tissues and cells of different sizes and magnification factors, with less memory usage and computation compared with the state-of-the-art transformers. To verify the generalization ability of the CSF transformer, experiments are performed on four public datasets of different organs and magnification factors. The CSF transformer outperforms the state-of-the-art task specific methods, convolutional neural network-based methods and transformer-based methods. The source code will be available in our GitHub <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nchucvml/CSFT</uri> .
Pengembangan Prototipe Kamus CCU (Cross Cultural Understanding) bagi Pemelajar BIPA (Bahasa Indonesia bagi Penutur Asing) dari China

This study aims to develop a prototype CCU dictionary for BIPA students from China. In addition to language, BIPA students need to understand Indonesian culture because language and culture are related. Language can be mastered well if the culture that accompanies it is also well understood. This research is a mixed study with the ADDIE model development method (Analysis, Design, Development, Implementation, and Evaluation) to the development stage due to the limited time in this study. Needs analysis is done by using questionnaires and literature study. Then, the design and development of this prototype is carried out by taking into account the aspects of the material, presentation, as well as language and readability. The results of this study revealed that most BIPA students experienced culture shock because of the differences in Indonesian culture and the culture of the student country. There are 45 cross cultural understandings that BIPA students from China need to understand. The cross cultural understanding consists of 16 cultural similarities and 29 cultural differences. These cultural comparisons are presented in this developed CCU dictionary prototype. This CCU dictionary prototype is designed to be a material for creating applications as electronic learning media that can be accessed offline and contain images, audio, audio visuals, and text in it. This CCU dictionary prototype contributes to the BIPA teaching and learning process and it is beneficial for students, teachers, and institutions.
CPD 2023: The 6th International Workshop on Combining Physical and Data-Driven Knowledge in Ubiquitous Computing

With the proliferation of connected devices with advanced sensing, computing, and communication capabilities, ubiquitous computing systems have become prevalent nowadays. They have the potential to revolutionize various industries by enabling new applications and services (e.g., patient monitoring, personalized recommendations, traffic control, home energy management). However, in real-world ubiquitous computing systems, data collection can be expensive or impossible. Due to the limited quantity and quality of data available, pure data-driven methods may not perform well. A promising approach to overcome these limitations is to utilize physical knowledge, including domain knowledge from experts, heuristics based on experience, and analytic models of physical phenomena.