Reviewerly/exHarmony huggingface dataset:
Lexical cohesion development in English as a foreign language learners' argumentative writing: A latent class growth model approach

The current study explored the longitudinal development of lexical cohesion for speakers of English as a foreign language (EFL) at the group level by employing Latent Class Growth Modelling (LCGM). A convenience sample of 276 university students from Southwest China were recruited and invited to write four argumentative essays over four months. Their essays formed a learner corpus, which was utilized to construct unconditional latent class growth models to explore the latent classes of EFL learners' development trajectories of lexical cohesion in their writing. Besides, conditional LCGM including logistic regression analysis was employed to investigate the effect of English proficiency on the classifications. The unconditional models demonstrated that there were different latent classes of development trajectories for local and global cohesion indices, which supports the heterogeneity of their development trajectories. Those models also revealed that two categories were the optimal ones for latent classes of development trajectories for selected local and global lexical cohesion indices. Additionally, logistic regression analyses showed that English proficiency affected latent classes of development trajectories of only one global cohesion index. The implications for EFL/L2 writing instruction and language development research are also discussed.
A supervised generative optimization approach for tabular data

Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the consensus on which method we should use for the specific data sets and use cases remains challenging. Moreover, the majority of existing approaches are “unsupervised” in the sense that they do not take into account the downstream task. To address these issues, this work presents a novel synthetic data generation framework. The framework integrates a supervised component tailored to the specific downstream task and employs a meta-learning approach to learn the optimal mixture distribution of existing synthetic distributions.
A RGB image encryption technique using chaotic maps of fractional variable-order based on DNA encoding

This paper presents a controller-based synchronization scheme in a leader-follower architecture for chaotic maps of fractional variable-order. The proposed scheme demonstrates successful synchronization of three different chaotic maps through simulation results. The implementation on Arduino UNO boards is used to showcase the relatively low algorithm complexity of the proposed scheme. Additionally, the paper explores a potential application of the proposed scheme in an RGB image encryption system, leveraging the chaotic behavior of the system for pseudo-random sequence generation. The encoding scheme utilizes the complementary property of Deoxyribonucleic acid (DNA) molecule bases. The presented encryption scheme employs the variable-order of the fractional derivative as a key for improved safety. This enhancement improves the methodology resistance against brute-force attacks while maintaining resilience against other cryptoanalysis techniques.
LayerDiffusion: Layered Controlled Image Editing with Diffusion Models

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semantic-based layered controlled image editing method. Our method enables non-rigid editing and attribute modification of specific subjects while preserving their unique characteristics and seamlessly integrating them into new backgrounds. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy combined with layered diffusion training. During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description. Experimental results demonstrate the effectiveness of our method in generating highly coherent images that closely align with the given textual description. The edited images maintain a high similarity to the features of the input image and surpass the performance of current leading image editing methods. LayerDiffusion opens up new possibilities for controllable image editing.
Anomaly detection method based on penalty least squares algorithm and time window entropy for Cyber–Physical Systems

Real-time system status detection must be accurate and reliable due to the close coupling of Cyber-Physical Systems (CPS) components. In order to improve the effectiveness of the CPS anomaly detection method, this paper proposes a real-time detection method based on the least squares algorithm and conditional entropy. To address the issue of overfitting and insufficient generalization of least squares, the penalty term of least squares is optimized by two different functions. Meanwhile, the K-S test is introduced in the time window entropy detection model to tackle the problem of setting threshold. The two detection mechanisms proposed operate in parallel and the initial localization of anomaly source is achieved by the time window entropy mechanism. The proposed method for anomaly detection is assessed using six evaluation metrics and demonstrates an accurate and effective detection capability on both the SWaT dataset and the CICIDS2017 dataset.
Modern Faces of Filtration

The filtration method for proving decidability in a focused minimal manner is a highlight of modal logic, widely used, but also posing a bit of a challenge as to its scope and what makes it tick. In this paper, we bring together a number of modern perspectives on filtration, including model-theoretic and proof-theoretic ones. We also include a few more unusual recent connections with dynamic logics of model change and logics of questions and issues. Finally, we analyze where the filtration method fails in full first-order logic, and what it still has to say there.
A Sentiment Analysis Model for the COVID-19 Vaccine in Indonesia Using Twitter API v2, TextBlob, and Googletrans

COVID-19 was declared a world pandemic in early-mid 2020. After about a year, several vaccines for this virus have been found and become alternative solutions to reduce the spread of the COVID-19 pandemic and build up herd immunity in society. Since early 2021, Indonesia has been one of the countries that participate in using vaccines for the public to fight the COVID-19 pandemic. However, there are a lot of positive and negative responses from Indonesian society related to these COVID-19 vaccines. Implementing a sentiment analysis model for a specific topic like a “vaccine” from social media could help us to see and understand the responses from society in Indonesia towards the vaccine program that is being conducted by the Indonesian government. Understanding society's response towards vaccines is expected to be able to support the Indonesian government, for example in formalizing the distribution strategy of vaccines in the future. This paper discusses how to develop a sentiment analysis model, by implementing several existing technologies such as Twitter API, TextBlob, and Googletrans Python libraries. The utilization of these existing technologies could show how a sentiment analysis model could be developed conveniently, for example in using cases to analyse Indonesian society's responses towards the COVID-19 vaccine program. Besides the solution design, this paper also shows a sample of data visualization of the sentiment analysis model in a meaningful infographics format.
Application of virtual reality in diverse fields of study in education sector: a systematic literature review

Technology is a form of non-living thing that supports and carries out the needs of human life. One type of technology is Virtual Reality (VR), which is already familiar to many people. According to Augmented and Virtual Reality Report conducted by Perkins Coie in 2020, education (28%) is the third sector that is expected to implement Virtual Reality (VR) the most, still behind gaming (47%) and health care devices (38%). The goal of this research is to generate insights about existing Virtual Reality (VR) implementations to encourage more implementation of Virtual Reality (VR) specifically in the education sector. We believe that Virtual Reality (VR) can be used to leverage learning in various fields of education. This research adopts Systematic Literature Review as a research method. Total of 200 published VR-related studies from 2019 to 2023 through Google Scholar, ScienceDirect, and ACM Digital Library, resulting from searches, then being reviewed, analyzed, and synthesized. The research concludes that there are ten fields (Arts, Engineering, History, Languages and Literatures, Life Sciences, Math and Computation, Medical, Physical Education, Physical Sciences, Qualitative Social Sciences) of study that apply Virtual Reality (VR) with Medical (77 study), Arts (24 study), and Math and Computation (18 study) hold the top three spots for the most number of studies done, the characteristic from field of study that suitable for Virtual Reality (VR) is the field of study that have practices which could be mirrored from real-life practice, three factors that influence the implementation of Virtual Reality in the education sector are engaging, imaginative, and precautionary while some of Virtual Reality (VR) benefits for learning are for attention training and to increase problem solving skills, and Virtual Reality (VR) learning is more effective compared to traditional learning based on empirical findings. Therefore, in this research, we discuss an overview towards the application of Virtual Reality (VR) in diverse fields of study in the education sector based on previous studies.
Design and Development a Virtual Planetarium Learning Media Using Augmented Reality

The solar system has always been a mystery to many. If not for the advanced technologies, most humans would not have the opportunity to gain knowledge about the planets. Although Earth is a part of the solar system, the solar system is simply too dangerous and expensive for humans to explore casually. Humans do not interact with the Sun or planets actively. This is especially concerning for children who often require visual aids in studying. An Augmented Reality (AR) based application can solve that problem. Through Virtual Planetarium, children may interact with the Sun or the planets and gain information. This will help aid children's guardians in studying the solar system. The application is made by Systems Development Life Cycle (SDLC) method. Through the making of this application, it is expected that children will have a better understanding of the solar system.
Face Mask Detection for COVID-19 Prevention using Computer Vision

During the height of the Coronavirus Disease (COVID-19) pandemic, prevention by obeying health protocol is a key to fight this pandemic, one of the ways is by using a face mask. In this paper, we conduct a research to make a face mask detection using Computer Vision. This face mask detection will detect people who use mask or not using mask by using Convolutional Neural Network (CNN) Algorithm, using three models, namely MobileNetV2, ResNet50V2, and VGG16, which are trained as many as 20 epochs for each model. The result showed MobileNetV2 and Resnet50V2 have better performance in terms of accuracy and loss value than VGG16.
MAPoseNet: Animal pose estimation network via multi-scale convolutional attention

Animal pose estimation serves as an upstream task for recognizing and understanding animal behavior. Over the last year, the accuracy of the deep learning-based method has steadily improved, but at the expense of the model's inference speed. This paper uses an efficient and powerful model to improve inference speed and accuracy. The classic encoder–decoder architecture is chosen. For estimating animal pose, our model based on a feature pyramid and a multi-scale asymmetric convolution attention mechanism is developed and named MAPoseNet (Animal Pose Estimation Network Via Multi-scale Convolutional Attention). MAPoseNet consists of an encoder and a decoder. Rather than typical self-attention, the encoder's attention mechanism comprises multi-scale, asymmetric convolutions that are lightweight and instrumental in improving inference speed. A feature pyramid and a feature balance module make up the decoder. The public dataset AP-10K is used to train and test MAPoseNet. A series of experimental results demonstrate that the MAPoseNet model provides cutting-edge performance. MAPoseNet outperforms HRFormer by 1.3 AP and 0.8 AR, with 33.7% fewer FLOPs and 66% faster inference speed. And our model surpasses HRNet and HRFormer on the Animal Pose dataset as well. Our model has achieved a win-win situation regarding inference speed and accuracy.
Usability Analysis of Text Generation by ChatGPT OpenAI Using System Usability Scale Method

The development of artificial intelligence systems has resulted in various AI products including ChatGPT, which is a new product classified as a chatbot. This research aims to ensure that text generation systems such as ChatGPT open AI have the best level of quality and usability and are able to provide a satisfying experience for users. To measure and evaluate the effectiveness, efficiency and user satisfaction of the ChatGPT platform, researchers used the System Usability Scale (SUS) method. This data collection was carried out using an online questionnaire. After the collected data has been tested for validity and reliability, the researchers then analyzed the data results. From the results of the research conducted, the SUS value of the ChatGPT platform is 67.44. This score is included in the marginal high category of class D, with a reasonable or sufficient interpretation. With the results of the analysis per question item, it shows that users tend to agree that the system runs quite effectively, efficiently, well and is easy to understand. Although ChatGPT is able to perform tasks or commands well. However, it should be noted that not all information loaded by ChatGPT is presented in a complete, current and correct manner. This is because the information presented by ChatGPT is only limited to 2021. Because ChatGPT is a new technology and is still under development, further researchers are expected to test other features or ChatGPT to ensure the stability and reliability of the entire ChatGPT system using other research methods.
Development of Android-Based Learning Media for High School Students in Indonesia: A Systematic Review of Literature

This study aims to comprehensively examine research on developing android-based learning media(ABLM) in Indonesia from 2015 to 2022. The research method used is the SLR to explore 32 articles from the accreted journals Sinta 1 and Sinta 2 by the Kemristekdikti RI with the keyword "Android" from 2015 to 2022. The results show that the trend of ABLM development research in Indonesia in the last eight years has continued to increase. The ADDIE model has been the most widely used research and development (RnD) method over the previous eight years. The questionnaire is the most dominant data collection tool used in Indonesia. The most researched subject related to research on developing ABLM in Indonesia is chemistry. The Indonesian Science Education Journal is the journal that has contributed the most to publishing ABLM development articles. The theory used in ABLM development research is problem-solving, critical thinking, and self-diagnosis skills. The study indicates that ABLM influences problem-solving (PS) abilities, critical thinking, and self-diagnosis. Meanwhile, a brief outline of the ABLM procedure for development for further study was presented in this paper Keywords: Developing Learning Media, Android-Based Learning, Learning Media
Evaluating and Establishing Baseline Security Requirements in Cloud Computing: An Enterprise Risk Management Approach

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Post-quantum distributed ledger technology: a systematic survey

Blockchain technology finds widespread application across various fields due to its key features such as immutability, reduced costs, decentralization, and transparency. The security of blockchain relies on elements like hashing, digital signatures, and cryptography. However, the emergence of quantum computers and supporting algorithms poses a threat to blockchain security. These quantum algorithms pose a significant threat to both public-key cryptography and hash functions, compelling the redesign of blockchain architectures. This paper investigates the status quo of the post-quantum, quantum-safe, or quantum-resistant cryptosystems within the framework of blockchain. This study starts with a fundamental overview of both blockchain and quantum computing, examining their reciprocal influence and evolution. Subsequently, a comprehensive literature review is conducted focusing on Post-Quantum Distributed Ledger Technology (PQDLT). This research emphasizes the practical implementation of these protocols and algorithms providing extensive comparisons of characteristics and performance. This work will help to foster further research at the intersection of post-quantum cryptography and blockchain systems and give prospective directions for future PQDLT researchers and developers.
A Meta-Analytic Examination of the Cost and Benefits of Overlay versus Separate Displays

Costs and benefits are associated with how information is presented depending on the type of task required. A meta-analysis was conducted to examine the costs and benefits associated with overlaying information on a single display, as in the case with a head-mounted display, or presenting information more separately, as in the case with a head-down display (e.g., a tablet). Seventeen studies contributed a total of 82 individual effect sizes that met the inclusion criteria for the meta-analysis. Overall, overlaid displays led to a response time benefit for integration tasks without compromising accuracy, whereas separate displays led to an accuracy benefit for focused attention tasks. The findings here speak favorably to the use of head-mounted displays for tasks that require integrating information on the display with information in the real world.
Adaptive CNN-Based Image Compression Model for Improved Remote Desktop Experience

This paper addresses the optimization of desktop image presentation in remote desktop scenarios. Remote desktop tools, essential for work efficiency, often employ image compression to manage bandwidth. While JPEG is a prevalent choice due to its efficiency in eliminating redundancy, it can introduce artifacts as compression increases. Recently, deep learning-based compression techniques have emerged, rivaling traditional methods like JPEG. This research introduces a convolutional neural network-based model for image compression and reconstruction, emphasizing human visual perception. By integrating adaptive spatial and channel attention mechanisms, it ensures better preservation of text and texture. This method outperforms JPEG and other deep learning algorithms in image quality and compression ratio.
Exploiting Partial Common Information Microstructure for Multi-modal Brain Tumor Segmentation

Learning with multiple modalities is crucial for automated brain tumor segmentation from magnetic resonance imaging data. Explicitly optimizing the common information shared among all modalities (e.g., by maximizing the total correlation) has been shown to achieve better feature representations and thus enhance the segmentation performance. However, existing approaches are oblivious to partial common information shared by subsets of the modalities. In this paper, we show that identifying such partial common information can significantly boost the discriminative power of image segmentation models. In particular, we introduce a novel concept of partial common information mask (PCI-mask) to provide a fine-grained characterization of what partial common information is shared by which subsets of the modalities. By solving a masked correlation maximization and simultaneously learning an optimal PCI-mask, we identify the latent microstructure of partial common information and leverage it in a self-attention module to selectively weight different feature representations in multi-modal data. We implement our proposed framework on the standard U-Net. Our experimental results on the Multi-modal Brain Tumor Segmentation Challenge (BraTS) datasets outperform those of state-of-the-art segmentation baselines, with validation Dice similarity coefficients of 0.920, 0.897, 0.837 for the whole tumor, tumor core, and enhancing tumor on BraTS-2020.
Genetic Programming Symbolic Regression with Simplification-Pruning Operator for Solving Differential Equations

Differential equations (DEs) are important mathematical models for describing natural phenomena and engineering problems. Finding analytical solutions for DEs has theoretical and practical benefits. However, traditional methods for finding analytical solutions only work for some special forms of DEs, such as separable variables or transformable to ordinary differential equations. For general nonlinear DEs, analytical solutions are often hard to obtain. The current popular method based on neural networks requires a lot of data to train the network and only gives approximate solutions with errors and instability. It is also a black-box model that is not interpretable. To obtain analytical solutions for DEs, this paper proposes a symbolic regression algorithm based on genetic programming with the simplification-pruning operator (SP-GPSR). This method introduces a new operator that can simplify the individual expressions in the population and randomly remove some structures in the formulas. Moreover, this method uses multiple fitness functions that consider the accuracy of the analytic solution satisfying the sampled data and the differential equations. In addition, this algorithm also uses a hybrid optimization technique to improve search efficiency and convergence speed. This paper conducts experiments on two typical classes of DEs. The results show that the proposed method can effectively find analytical solutions for DEs with high accuracy and simplicity.