Reviewerly/exHarmony huggingface dataset:
PENERAPAN METODE LEAST SQUARE UNTUK PREDIKSI PENJUALAN BRIGHT GAS 5,5 KG

This company sells 5.5 kg of Bright gas which will be distributed to the base every month, experiencing ups and downs. In addition, it also resulted in the inappropriate procurement of 5.5 kg Bright gas. Every month it is not adjusted to monthly sales estimates because it has not used a prediction system. Therefore, there is a sales prediction system of 5.5 kg of bright gas every month, the amount of bright gas is 5.5 kg which will be distributed to the base. The purpose of this research is to find out good accuracy in the Least Square method for the selling process of 5.5 kg bright gas at PT. Togo Jaya Gorontalo. Results achieved With the bright gas prediction system, predictions can be made for the next period and measurement results using MAPE of 0.20%.
APLIKASI SISTEM INFORMASI PENGAJUAN CUTI BERBASIS WEBSITE “SIPECUT”

The competitive era of Industrial Revolution 4.0 is characterized by the development of progress in all fields of activity, with companies and authorities competing to keep up with the changes through the use of information technology. One of the most popular information technology implementations in enterprises is the employee information system for vacation. A manual leave application system is still used to apply for leave at the East Java provincial government office. An employee who wants to apply for leave must first print the leave form, fill out the leave form, and then give the leave form to the employee for manager approval. The manual process slows down the process of requesting time off. The purpose of this research is to address the shortcomings of the current manual system and create a web-based leave request information system aimed at simplifying and speeding up the employee leave request process. The research method used by researchers in this study is a qualitative descriptive method. This web-based leave request information system can be used to assist employees in requesting leave and make it easier for companies to process and manage leave data.
Comprehending irony in text: Evidence from scanpaths

Eye-tracking studies have shown that readers reread ironic phrases when resolving their meaning. Moreover, it has been shown that the time-course of processing ironic meaning is affected by readers working memory capacity (WMC). Irony is a context-dependent phenomena but using traditional eye-movement measures it is difficult to analyze processing beyond sentence-level. A promising method to study individual differences in irony processing at the paragraph-level is scanpath analysis. In the present experiment, we analyzed whether individual differences in WMC are reflected in scanpaths during reading ironic stories by combining data from two previous eye-tracking studies (N=120). The results revealed three different reading patterns: fast-and-linear reading, and selective and non-selective rereading. The readers predominantly used fast-and-linear reading pattern for ironic and literal stories. However, readers were less likely to use non-selective rereading pattern with ironic than literal texts. The reading patterns for ironic stories were modulated by WMC. Results showed that scanpaths captured differences missed by standard measures, showing it to be a valuable tool to study individual differences in irony processing.
Academic writing and AI: Day-3 experiment with eco-surplus culture

To check the ability of AI to identify precise and detailed scientific information, I experiment with how accurate AI recognizes scientific terms, their origins, meanings, and usages, and whether the accuracy increases over time.
Analysis of Curriculum Planning by Using Importance Performance Analysis Technique

Curriculum is an essential part of education. It has two functions in education, that are to realize the vision of education as well as become the reference of the implementation of education. Because of the importance of curriculum, school as an institution that manages the process of education must carry out curriculum planning that can be perceived by the teachers so that it can be implemented in the teaching and learning process. The objective of this research was to analyze the curriculum planning based on the importance and performance by Importance Performance Analysis. The data collection was done by random questionnaire given to 30 teachers in an elementary school, SD Islam dan Leadership Bintang Cendekia. From this research, it was found 8 very satisfying important attributes, 4 poor important attributes, and 6 poor unimportant attributes, and 2 very satisfying unimportant attributes. Keywords: Analysis of planning, curriculum, Importance Performance Analysis (IPA).
Combing Learners’ Challenges in The Learning of Literature in Malaysian ESL Classrooms: A Review

Incorporating literature in English language classrooms has been long implemented worldwide.Its benefits are undeniable since many past studies have proven that it could help the learners of English as a Second Language (ESL) improve their language proficiency.Despite many of the benefits shown, there are some difficulties encountered in the learning of literature in ESL classrooms.This review addressed two research questions, namely (a) examining the challenges learners encounter when learning English literature in an ESL classroom and (b) identifying alternative ways in learning literature.Around 40 research studies from the year of 2015 -2022 and four (4) of past pertinent studies ranging from 2000 -2010 were shortlisted from two databases, Google Scholar and UKM e-journal based on these two review questions.This review explored the challenges of learners when learning literature in ESL classrooms under three key concepts which are literary text, cultural differences, and learner's attitude toward learning literature.This review further explored the alternative ways to learn literature which revealed two approaches through mapping and using multimodality as a medium of learning.This study then proposes further research on overcoming the challenges and maximising future research on the diversity of teaching and learning approaches in all feasible ways.
Syllabus Concepts, Approaches and Types: A Theoretical Account

Language syllabus is a crucial element in the educational process.It is based on a decision about the "units" of classroom activity and the "sequence" in which they are to be performed.However, the researchers see that syllabus design is neglected nowadays, and it needs more light shading on.So, in this study, the researchers present the concepts of the syllabus, then, the most influential approaches to the syllabus are accounted for.Last, the researchers shed light on the basic types of syllabi be applied in the classroom.After stating and observing the theoretical account, the researchers conclude that the syllabus concepts are very crucial area needs to focus on as it represents a guide for both the teacher and the student in the classroom setting.Moreover, there are many syllabus types with a set of concepts; each leads to a specific type of syllabus.Last, according to the approaches stated, there are six syllabus types that most educationalists agreed upon.However, in real practice, more than one type of syllabus is applied.
Multi Label Automatic Image Annotation Neural Network to Handle Multi Media Image Retrieval

The present-day business online web indexes have embraced electronic picture search to further develop precision in picture information recovery. However Re-positioning is expectedly considered as a successful cycle for deciding the situation with electronic picture web search tools, yet it experiences a lack of a couple of. Consequently, some grouping methods, particularly (Novel Image Re-positioning System) NIRS have to be proposed to carry out inquiry picture re-positioning with semantic marks in electronic picture information recovery, which naturally recovers results in view of visual semantic highlights for various question or catchphrase extensions. To get to productive picture with the annotation is an aggressive concept in present. So that in the present paper, we are going to propose the Unsupervised Multi Labeled Image Annotate Learning Approach (UMLIALA) to decrease complexity in indexing of image with mining of web related convex optimization and classify required image data from large image data sets. And also use group based approximation calculation to improve accuracy in retrieval of images from different image data sources. Experiments of proposed approach give better and efficient results when compare to traditional approaches in terms of different image exploration parameters studies on different large image data sets.
EFFECTIVENESS OF PBL-BASED MOBILE LEARNING TO IMPROVE STUDENTS' CRITICAL THINKING SKILLS

The purpose of this research is to evaluate whether or not students in the eighth grade at SMP Negeri 2 Betara in the Tanjung Jabung Barat Regency can improve their mathematical critical thinking skills by using mobile learning that is based on problem-based learning (PBL). The participants in this research were all eighth-graders from one junior high school in the Tanjung Jabung Barat Regency. There were a total of 128 students included in this study, and they were distributed across four classes. The sample was selected from as many as two classes, specifically VIIIA with a total of 28 students serving as the experimental group and VIIIB with a total of 28 students serving as the control group. The analysis of the data reveals that the Zcount value is 10,099, and the significance value is 0.000&lt; 0.005; these values were determined to be significant. Therefore, the working hypothesis or alternative hypothesis that was proposed in this study is accepted. It states that there is a difference in the average critical thinking of students who use PBL-based mobile learning compared to students who use standard teaching materials. While the null or nil hypothesis, which states that "there is no difference in the average critical thinking of students who use PBL-based mobile learning and students who use conventional teaching materials," is rejected.
Acknowledgment to the Reviewers of Axioms in 2022

High-quality academic publishing is built on rigorous peer review [...]
Liouvillian Systems and Cryptography

In this chapter we show the use of a Liouvillian chaotic system on a master-slave scheme for secure communications. Two forms of data encryption are presented, the first is a state observer based on the Super-Twisting algorithm, the second is a receiver designed through the properties of Liouvillian systems, this last receiver does not suffer from data loss due to synchronization error present on state observer based encryption. To test the advantages of this encryption, images and text will be used as transmitted data.
Non-classical Correlations of Accelerated Observers Interacting with a Classical Stochastic Noise

Abstract The influence of a classical environment on the behaviour of the Wigner function and the concurrence of an accelerated two-qubit system interacting is discussed. The negative values of the Wigner function verify the non-classicality, while the concurrence shows the entanglement degree of the quantum system. We assume one or both observers are accelerated (s) and interact with a common or independent environment. Suppression of the coherence rises due to acceleration effect, noise strength, and noise frequency are pointed. The amount of quantum entanglement is investigated by using the quantum concurrence, where its positive values are consistent with what the negative Wigner function shows. We found that the degradation of coherence of the initial system reduces when observers interact with their environments independently more than when it interacts with a common one. It is also sensitive to the acceleration status of both observers. Additionally, the correlation and entanglement pertinent to the situation with common or independent noise agents behave contrarily for the two regimes of Markovian and non-Markovian noises.
Muti-class Image Classification using Transfer Learning

Abstract: Humans are very proficient at perceiving natural scenes and understanding their contents. Everyday image content across the globe is rapidly increasing and there is a need for classifying these images for further research. Scene classification is a challenging task, because in some natural scenes there will be common features in images and some images may contain half indoor and half outdoor scene features. In this project we are going to classify natural scenery in images using Artificial Intelligence. Based on the analysis of the error backpropagation algorithm, we propose an innovative training criterion of depth neural network for maximum interval minimum classification error. At the same time, the cross entropy and M3CE are analyzed and combined to obtain better results. Finally, we tested our proposed M3 CE-CEc on two deep learning standard databases, MNIST and CIFAR-10. The experimental results show that M3 CE can enhance the cross-entropy, and it is an effective supplement to the cross-entropy criterion. M3 CE-CEc has obtained good results in both databases.
A Scalable Technique for Weak-Supervised Learning with Domain Constraints

We propose a novel scalable end-to-end pipeline that uses symbolic domain knowledge as constraints for learning a neural network for classifying unlabeled data in a weak-supervised manner. Our approach is particularly well-suited for settings where the data consists of distinct groups (classes) that lends itself to clustering-friendly representation learning and the domain constraints can be reformulated for use of efficient mathematical optimization techniques by considering multiple training examples at once. We evaluate our approach on a variant of the MNIST image classification problem where a training example consists of image sequences and the sum of the numbers represented by the sequences, and show that our approach scales significantly better than previous approaches that rely on computing all constraint satisfying combinations for each training example.
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

Coordination is one of the most difficult aspects of multi-agent reinforcement learning (MARL). One reason is that agents normally choose their actions independently of one another. In order to see coordination strategies emerging from the combination of independent policies, the recent research has focused on the use of a centralized function (CF) that learns each agent's contribution to the team reward. However, the structure in which the environment is presented to the agents and to the CF is typically overlooked. We have observed that the features used to describe the coordination problem can be represented as vertex features of a latent graph structure. Here, we present TransfQMix, a new approach that uses transformers to leverage this latent structure and learn better coordination policies. Our transformer agents perform a graph reasoning over the state of the observable entities. Our transformer Q-mixer learns a monotonic mixing-function from a larger graph that includes the internal and external states of the agents. TransfQMix is designed to be entirely transferable, meaning that same parameters can be used to control and train larger or smaller teams of agents. This enables to deploy promising approaches to save training time and derive general policies in MARL, such as transfer learning, zero-shot transfer, and curriculum learning. We report TransfQMix's performances in the Spread and StarCraft II environments. In both settings, it outperforms state-of-the-art Q-Learning models, and it demonstrates effectiveness in solving problems that other methods can not solve.
Randomization Tests for Adaptively Collected Data

Randomization testing is a fundamental method in statistics, enabling inferential tasks such as testing for (conditional) independence of random variables, constructing confidence intervals in semiparametric location models, and constructing (by inverting a permutation test) model-free prediction intervals via conformal inference. Randomization tests are exactly valid for any sample size, but their use is generally confined to exchangeable data. Yet in many applications, data is routinely collected adaptively via, e.g., (contextual) bandit and reinforcement learning algorithms or adaptive experimental designs. In this paper we present a general framework for randomization testing on adaptively collected data (despite its non-exchangeability) that uses a novel weighted randomization test, for which we also present novel computationally tractable resampling algorithms for various popular adaptive assignment algorithms, data-generating environments, and types of inferential tasks. Finally, we demonstrate via a range of simulations the efficacy of our framework for both testing and confidence/prediction interval construction.
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies

Interactive Artificial Intelligence (AI) agents are becoming increasingly prevalent in society. However, application of such systems without understanding them can be problematic. Black-box AI systems can lead to liability and accountability issues when they produce an incorrect decision. Explainable AI (XAI) seeks to bridge the knowledge gap, between developers and end-users, by offering insights into how an AI algorithm functions. Many modern algorithms focus on making the AI model "transparent", i.e. unveil the inherent functionality of the agent in a simpler format. However, these approaches do not cater to end-users of these systems, as users may not possess the requisite knowledge to understand these explanations in a reasonable amount of time. Therefore, to be able to develop suitable XAI methods, we need to understand the factors which influence subjective perception and objective usability. In this paper, we present a novel user-study which studies four differing XAI modalities commonly employed in prior work for explaining AI behavior, i.e. Decision Trees, Text, Programs. We study these XAI modalities in the context of explaining the actions of a self-driving car on a highway, as driving is an easily understandable real-world task and self-driving cars is a keen area of interest within the AI community. Our findings highlight internal consistency issues wherein participants perceived language explanations to be significantly more usable, however participants were better able to objectively understand the decision making process of the car through a decision tree explanation. Our work also provides further evidence of importance of integrating user-specific and situational criteria into the design of XAI systems. Our findings show that factors such as computer science experience, and watching the car succeed or fail can impact the perception and usefulness of the explanation.
Text to Point Cloud Localization with Relation-Enhanced Transformer

Automatically localizing a position based on a few natural language instructions is essential for future robots to communicate and collaborate with humans. To approach this goal, we focus on the text-to-point-cloud cross-modal localization problem. Given a textual query, it aims to identify the described location from city-scale point clouds. The task involves two challenges. 1) In city-scale point clouds, similar ambient instances may exist in several locations. Searching each location in a huge point cloud with only instances as guidance may lead to less discriminative signals and incorrect results. 2) In textual descriptions, the hints are provided separately. In this case, the relations among those hints are not explicitly described, leading to difficulties of learning relations. To overcome these two challenges, we propose a unified Relation-Enhanced Transformer (RET) to improve representation discriminability for both point cloud and natural language queries. The core of the proposed RET is a novel Relation-enhanced Self-Attention (RSA) mechanism, which explicitly encodes instance (hint)-wise relations for the two modalities. Moreover, we propose a fine-grained cross-modal matching method to further refine the location predictions in a subsequent instance-hint matching stage. Experimental results on the KITTI360Pose dataset demonstrate that our approach surpasses the previous state-of-the-art method by large margin.
Learnable Heterogeneous Convolution: Learning both topology and strength

Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism. In the experiments, our method either reduces VGG16/19 and ResNet34/50 computation by nearly 5x on CIFAR10 and 2x on ImageNet without harming the performance, where the weights are compressed by 10x and 4x respectively; or improves the accuracy by up to 1.0% on CIFAR10 and 0.5% on ImageNet with slightly higher efficiency. The code will be available on www.github.com/Genera1Z/LearnableHeterogeneousConvolution.
LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility

Learning to recover clear images from images having a combination of degrading factors is a challenging task. That being said, autonomous surveillance in low visibility conditions caused by high pollution/smoke, poor air quality index, low light, atmospheric scattering, and haze during a blizzard becomes even more important to prevent accidents. It is thus crucial to form a solution that can result in a high-quality image and is efficient enough to be deployed for everyday use. However, the lack of proper datasets available to tackle this task limits the performance of the previous methods proposed. To this end, we generate the LowVis-AFO dataset, containing 3647 paired dark-hazy and clear images. We also introduce a lightweight deep learning model called Low-Visibility Restoration Network (LVRNet). It outperforms previous image restoration methods with low latency, achieving a PSNR value of 25.744 and an SSIM of 0.905, making our approach scalable and ready for practical use. The code and data can be found at https://github.com/Achleshwar/LVRNet.
TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework

TeD-Q is an open-source software framework for quantum machine learning, variational quantum algorithm (VQA), and simulation of quantum computing. It seamlessly integrates classical machine learning libraries with quantum simulators, giving users the ability to leverage the power of classical machine learning while training quantum machine learning models. TeD-Q supports auto-differentiation that provides backpropagation, parameters shift, and finite difference methods to obtain gradients. With tensor contraction, simulation of quantum circuits with large number of qubits is possible. TeD-Q also provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.