Reviewerly/exHarmony huggingface dataset:
PENERAPAN METODE STRUCTURAL EQUATION MODELLING-PARTIAL LEAST SQUARES (SEM-PLS) DALAM MENGEVALUASI FAKTOR-FAKTOR YANG MEMPENGARUHI PDRB DI INDONESIA

Structural Equation Modeling (SEM) is a multivariate statistical analysis method that combines regression analysis with factor analysis. SEM can be used to describe simultaneous linear relationships between observed variables (indicators) and variables that cannot be directly measured (latent variables). In the development of covariance-based SEM, there are still weaknesses based on parametric assumptions that must be met in regression analysis, and one of the classic assumptions that must be met is the assumption that the data is normally distributed. Partial Least Square (PLS) is one solution or alternative method of model estimation to manage SEM modeling with reflective or formative indicators. PLS was created to overcome the limitations of the SEM method. Structural Equation Modeling-Partial Least Square (SEM-PLS) is a powerful analysis method because it allows structural equation modeling with the assumption that the data used does not have to be normally distributed, SEM-PLS can use a relatively small sample size, and the indicators used are reflective, formative, or a combination of both. This study aims to determine the effect of latent variable indicators, namely public service expenditure, economy, health, and education on the Gross Regional Domestic Product (GRDP) in each district/city in South Sulawesi Province in 2022. The indicators used for each latent variable are Public services (employee expenditure, goods and services expenditure, capital expenditure, other expenditure), Economy (goods and services expenditure, capital expenditure), Health (employee expenditure, goods and services expenditure, capital expenditure), Education (employee expenditure, goods and services expenditure, capital expenditure, other expenditure). The results of the study show that the other expenditure indicator on the latent variable of public services and the other expenditure indicator on the latent variable of Education are excluded in the study because they do not pass the loading factor test. The model equation obtained is GRDP = 0.052 Economy - 0.087 Health + 0.321 Education + 0.706 Public Services. The R2 value obtained from the model equation is 0.982, which means that the latent variables of public service expenditure, economy, health, and education can explain the latent variable of GRDP by 98.2%. The latent variables that significantly influence the model equation are public service expenditure and education.
O claro é certo? Os marcadores discursivos claro (PT) e certo (ITA): estudo contrastivo

O claro é certo? Discourse markers claro (PT) and certo (ITA): a contrastive study. The present study aims to compare the dialogical uses of two discourse markers (DM), namely the European Portuguese claro and the Italian certo, by analysing the data extracted from two corpora of spoken language (the Corpus de Referência do Português Contemporâneo and the KIParla corpus). Both claro and certo are part of the group of conversational markers of epistemic modality, which includes a vast set of linguistic expressions revolving around the semantics of "right", "clear", and "obvious". This study shows that in European Portuguese, claro is the most frequent and polysemic DM (the “pivot form”), playing different functions in the construction of interpersonal cooperation and negotiation of meaning: in dialogical contexts, claro can mark cooperative attention, emphatic response, or agreement. To mark such functions, claro can occur within different syntactic and functional patterns, such as repetitions and co-occurrence with other DMs. In the comparison with Italian, data show that certo functions similarly in a great extent of communicative situations, thus concluding that semantic and functional parallelism can be drawn between the two DMs. However, a noteworthy difference in use relies on the speakers’ choice of DM during their communicative exchanges: in European Portuguese, speakers seem to utter different epistemic DMs in a sequence of dialogical turns to signal the same functions, while it does not appear as natural in Italian. On the contrary, Italian speakers appear to use one epistemic DM at a time. This difference, although subtle, may constitute a critical area for translation. Keywords: discourse markers, epistemic discourse markers, dialogue, certo, claro.
Pengembangan Aplikasi Pembelajaran Mesin untuk Identifikasi Kemiripan Lukisan

This study has one goal, namely to be able to identify the similarity of paintings through painting imagery and from the image of the painting that has been input will show which artist's work is supposedly similar to the image that has been input. The algorithm used is a Convolutional Neural Network (CNN). This study used painting imagery data from Kaggle in the form of a folder. The image data used was 4299 which was divided into training data and testing data with a total of 3444 data from 11 classes and as testing data as many as 855 from 11 classes. The framework used in this study is ResNet-50 and the Convolutional Neural Network which is applied is Tensorflow Keras. From the results of the study, it has produced an accuracy value of 24% with an average probability of up to about 80% and above.
Peningkatan Keterampilan Literasi Membaca Permulaan Melalui Media Flash Card Pada Siswa Kelas 1 A UPT SPF Inpres Bira 2

The problem that is the background of this research is the low literacy skills of the beginning reading students in grade 1 A UPT SPF Inpres Bira 2. This study aims to describe the use of Flash Card media to improve literacy skills at the beginning of grade 1 A students at UPT SPF Inpres Bira 2. The research used was a type of classroom action research (PTK) which consisted of 2 cycles, each cycle consisting of 4 meetings with stages of activities in the form of planning, implementing, observing, and reflecting. The focus of this research is the use of Flash Card media to improve students' early reading literacy skills. The research subjects were class 1 A students at UPT SPF Inpres Bira 2 in the even semester of the 2023/2024 school year, which consisted of 16 students. Data collection techniques in this study used tests, observation, and documentation. Qualitatively the collection instrument used an observation sheet and quantitatively used a preliminary reading skills test. The data analysis technique used is descriptive quantitative and qualitative data analysis. The results showed that there was an increase in initial reading literacy skills in the learning process, as evidenced by the class average score of the pre-action was in the sufficient category (C), in cycle I it increased but was still in the sufficient category (C) while in cycle II experienced an increase and is in the good category (B). The increase is based on the initial reading literacy skill process using Flash Card media so that the initial reading literacy skills of class 1 A UPT SPF Inpres Bira 2 can experience an increase.
Application of Science and Technology of Goods Inventory Information System in the Ellisya Online Store

This research aims to identify business needs related to inventory management of goods at Ellisya stores. Developing a goods inventory information system starting from designing, developing and implementing an information system using information technology principles in managing goods inventory. Increase the accuracy of inventory predictions by assessing information systems' ability to predict inventory needs accurately. Assessing data security and protection by ensuring that the information system complies with data security standards. It provides adequate protection against potential threats to the security of inventory data. Provide recommendations for developing a website-based goods inventory information system. The research method used in this research is the Waterfall development method. This method was chosen because of its clarity and structured stages, which greatly support technology integration to increase inventory management efficiency and accuracy. The research results show that applying technological knowledge in the Goods Inventory Information System using the Waterfall method positively impacts operational efficiency, more accurate inventory predictions, data security, user satisfaction, and financial well-being. Therefore, applying science and technology is an important step to improve the performance and competitiveness of organizations in managing inventory. The research results provide practical implications for improving operational efficiency in inventory management. S&amp;T integration enables real-time monitoring, automation of administrative tasks, and more efficient stock management. The application of science and technology results in stronger data protection, having a positive impact on company reputation and customer trust. The novelty of this research lies in an integrative approach between the application of science and technology and the Waterfall development method. It provides a new perspective on how technology can be applied in the context of structured system development, namely by developing a website-based inventory information system for goods at Ellisya stores
Towards an Open Source-first Praxis in Libraries

In terms of utility and technical quality, open source software solutions have become a common option for many libraries. As barriers to adoption have been reduced and systems such as FOLIO appear poised to change the landscape of LIS technology, it is worth examining how the use of open source can support the normative core values of librarianship, and to outline a strategy for critical engagement with the technology that is beneficial to patrons and libraries. That strategy will require further codification, institutionalization and investigation of open source at many levels.
Nature as the Fecund Female in Joseph Conrads Heart of Darkness

Abstract: The Currency Detection Android Application with Image Processing is an innovative solution designed to empower visually impaired individuals and assist travelers in identifying and managing paper currency notes with accuracy and convenience. This application leverages advanced image processing techniques, including edge detection, color analysis, texture recognition, and optical character recognition (OCR), to extract crucial features from currency notes. Machine learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are employed to recognize currency denominations based on these extracted features. The application provides real-time audio feedback, announcing the recognized denomination, and features a user-friendly interface with voice-guided instructions, catering to both visually impaired and sighted users. A comprehensive currency library ensures coverage of major world currencies, with regular updates to accommodate new designs and security features. Offline functionality further enhances accessibility. The project's scope encompasses primary functionalities, target audience, supported currencies, technological considerations, and acknowledges project limitations.
Visualization of the Impact of Classroom Utterances Using Generative Dialogue Models

As teachers in elementary school classes have limited time for reflection, it is desirable for the reflection process to automated. Therefore, in this study, we analyze the utterances of teachers and children using a neural network based dialogue model. Additionally, we also analyze and visualize the degree of impact of the utterance during the utterance generation process.
A Case Study on the Comparison of AI-facilitated Threaded Conversation versus Threaded Conversation

In this paper, we present a process for collecting people's opinions by providing the public with and without an AI-facilitated online environment to post their views. Specifically, we targeted online communities on Facebook and directed them to D-Agree, an AI-powered online discussion forum. We want to explore the extent to which user participation in AI-assisted threaded conversation is successful by looking at the depth of threaded conversation while comparing threads not facilitated by AI facilitation. We deemed an AI facilitator to have successfully promoted the engagement if the discussion thread with an AI-facilitated thread had depth compared to discussion threads without an AI facilitator presence. Our analysis indicates that threaded conversation with conversational agent presence are successful. The collected insights can be used as a planning tool for developing conversational AI applications.
Qualitative Evaluations of Ideas created by Generative AI

This paper describes that the difference between GPT-3 and human in the qualitative evaluation of ideas. GPT-3 is expected to be used as ‘Artificial General Intelligence’ that requires no pre-training for a specific applications or purposes different from the conventional language model. This study aims to validate the usefulness of the ideas created by GPT-3. GPT-3 was validated using three pre-training approach; zero-shot, one-shot, and few-shot. The qualitative evaluation of ideas was conducted on three items; fluency, feasibility and originality. The comparative experiments were conducted on the evaluation results of GPT-3-created ideas and human-created ideas, as well as on the results of the in-context learning settings for tasks in GPT-3. The results suggested that human-created ideas were superior to GPT-3-created ideas in originality; moreover, few-shot is the highest of the approaches in originality.
Estimation of Court Boundary and Showing of Player Tra- jectory using a Broadcast Handball Game Video

This study proposes a method for estimating court boundary for player tracking and show- ing of player trajectory for visualization in a broadcast handball video. The video captured by a camera shows approximately one-third of the court, and shifts from left to right to fol- low the players. Therefore, the camera direction is estimated for the player trajectory with a taken goal object position in the video, and the goal object is detected with the object de- tection algorithm YOLOv5. The court boundary is estimated in two ways: one is by using the goal object detection result, and the other is by using a feature of a red painted area on the court. The player is detected and classified to own team by YOLOv5 and uniform color, and its trajectory is shown on a top view of the court. We experimented with three videos to confirm the accuracy of the two proposed methods of the court boundary estimation. The results indicates that the estimation rates of the two methods were 59.2 % and 73.8 %, respectively.
Identifying EOL Software

Throughout our project, we have been exploring ways to identify EOL and malicious websites using python. With this software, we can help people stay away from these types of websites. There are lots of cybersecurity problems in the world, but we wanted to address the EOL problem because it is something that is within the realm of what we are learning (language recognition), and it has a practical use in the world. We used multiple different commands to identify certain “keywords,” with the intention of getting the highest possible accuracy percentage. We also prioritized the elimination of false negatives over false positives. Creating a new variable with the keywords, we were able to correctly predict 99% of the EOL websites using the data given to us. We were able to conclude that mixing the 2 most important data columns and creating one variable to determine both of the variables is the best way to go about creating a variable that gives you the most.
An implementation of complex-valued matrix-vector multiplication based on MZI array for photonic neural networks

We propose a complex-valued matrix-vector multiplication method in this work, which make full use of the amplitude and phase information of input signal light and weight matrix. We demonstrate our computing method theoretically and experimentally. In order to verify our theory, a photonic chip is designed and used to setup a photonic neural network system, and the corresponding accuracies reach more than 90%.
Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework

In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. Nevertheless, mechanisms to hire sensing IoT devices to share their data with the VSP and then deliver the constructed digital twin to the Metaverse users are vulnerable to adverse selection problem. The adverse selection problem, which is caused by information asymmetry between the system entities, becomes harder to solve when the private information of the different entities are multi-dimensional. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem. To demonstrate the effectiveness of our algorithm, we conduct extensive simulations and measure several key performance metrics of the contract for the Metaverse. Our results show that our designed iterative contract is able to incentivize the participants to interact truthfully, which maximizes the profit of the VSP with minimal individual rationality (IR) and incentive compatibility (IC) violation rates. Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.
Spatio-Temporal Identity Multi-Graph Convolutional Network for Traffic Prediction in the Metaverse

The metaverse is at the forefront of the next-generation internet application, where billions of users seamlessly immerse themselves in a hybrid reality of physical-virtual worlds and switch between virtual environments thanks to reliable resource allocation and synchronization. However, the exponential growth of users and computationally intensive applications make joint optimization of multiple indicators challenging. Therefore, predicting user behavior is pivotal in assisting the optimization process. Although graph neural networks have demonstrated remarkable performance in traffic prediction, most existing schemes link nodes based on their distances and require significant computational resources, limiting their generalization and deployment in the metaverse. To solve this problem, we propose an efficient Spatio-temporal Identity Multi-graph convolutional network Framework (SIMF) for application-level traffic prediction in the metaverse. In the SIMF, we design a spatio-temporal embedding layer and multi-graph convolutional module to jointly capture spatio-temporal correlations among nodes (avatars) and reduce the dependence on topology information, which is more consistent with the real relationship between avatars in the metaverse. We conduct extensive experiments to evaluate the SIMF, which show that our proposed framework achieves superior accuracy even without graph information while maintaining low time complexity, making it suitable for traffic prediction in the metaverse.
SDSR: Optimizing Metaverse Video Streaming via Saliency-driven Dynamic Super-Resolution

Metaverse (especially 360-degree) video streaming allows broadcasting virtual events in the metaverse to a broad audience. To reduce the huge bandwidth consumption, quite a few super-resolution (SR)-enhanced 360-degree video streaming systems have been proposed. However, there is very limited work to investigate how the granularity of SR model affects the system performance, and how to choose a proper SR model for different video contents under diverse environmental conditions. In this paper, we first conduct a dedicated measurement study to unveil the impact of different granularities of SR models. It is found that the scene of a video largely determines the effectiveness of SR models in different granularities. Based on our observations, we propose a novel 360-degree video streaming framework with saliency-driven dynamic super-resolution, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDSR</i> . To maximize user QoE, we formally formulate an optimization problem and adopt the model predictive control (MPC) theory for bitrate adaptation and SR model selection. To improve the effectiveness of SR model, we leverage the saliency information, which well reflects users’ view interests, for model training. In addition, we reuse an SR model for similar chunks based on temporal redundancy of a video. Finally, we conduct extensive experiments on real traces and the results show that SDSR outperforms the state-of-the-art algorithms with an improvement up to 32.78% in terms of the average QoE.
An Adaptive and Modular Blockchain Enabled Architecture for a Decentralized Metaverse

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.
A Spatiotemporal Backdoor Attack Against Behavior-Oriented Decision Makers In Metaverse: From Perspective of Autonomous Driving

Behavior-oriented decision-makers are critical components in generating intelligent decisions for user virtual interactions in metaverse. In this work, we study the efficiency and security of behavior-oriented decision-makers in metaverse from perspective of autonomous driving (AD), where modeling human uncertain driving behaviors is the key factor of their performance. We first explore the ability of different deep-neural-network-based decision-makers used in deep reinforcement learning for efficient autonomous vehicle control, and then we propose a novel neural backdoor attack against them using spatiotemporal driving behaviors, rather than an immediate state. With our attack, the adversary acts as a normal driver and can trigger attacks by driving his vehicle following specific spatiotemporal behaviors. Extensive experiments show that our proposed backdoor attack can achieve high stealthiness and effectiveness (less than 1% clean performance variance rate and more than 98% attack success rate) on behavior-oriented decision-makers, and is sustainable against existing advanced defenses.