Reviewerly/exHarmony huggingface dataset:
Secondary Social Studies Teachers' Perception and Training Demand for AI-based Education

Objectives This study aims to investigate the attitudes and beliefs of secondary social studies teachers towards AI-based education and to assess their demand for training in this area.&#x0D; Methods To this end, a survey was administered to 1,164 middle and high school social studies teachers (civics, history, geography, and ethics). The survey consists of two parts: learning about AI (learning elements of AI and capabilities of AI) and learning with AI (difficulties of teaching with AI and demand for teacher training in AI-based education). First, the reliability coefficient of the questionary items was calculated, followed by descriptive statistical analysis. To verify the statistical significance of dfferences by teachers’ specialty, a one-way analysis of variance was performed, and the effect size was calculated to confirm the practical significance. For descriptive answers, the word frequency was calculated and visualized in a word cloud to identify prominent issues.&#x0D; Results 1) It was discovered that social studies teachers consider AI's social impact, machine learning, and data as crucial learning components. Particularly, teachers believed that students should be aware of artificial intelligence's technical limitations and ethical implications. 2) Assuming the class situation, the teachers indicated that the greatest anticipated challenge would be the lack of ‘exemplary practices for social studies classes using AI’. 3) Despite the fact that few teachers have prior experience with coding or AI training, 71.2% of all respondents agreed that teacher training program in AI-based education is necessary.&#x0D; Conclusions This study is anticipated to serve as a foundation for the development of AI-based training programs and the introduction of AI in education.
Hypothesis Testing

Hypothesis testing is a simple way to construct information about a dataset and to determine if that information is true or false. Another method to think about hypothesis testing is to use it as a way to decide whether an assumption about a dataset succeeds and is supported or fails and is not supported. It is used frequently in science and medicine to better understand the effects of drugs and treatments.
Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection

The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.
When Programs Have to Watch Paint Dry

Abstract We explore type systems and programming abstractions for the safe usage of resources. In particular, we investigate how to use types to modularly specify and check when programs are allowed to use their resources, e.g., when programming a robot arm on a production line, it is crucial that painted parts are given enough time to dry before assembly. We capture such temporal resources using a time-graded variant of Fitch-style modal type systems, develop a corresponding modally typed, effectful core calculus, and equip it with a graded-monadic denotational semantics illustrated by a concrete presheaf model. Our calculus also includes graded algebraic effects and effect handlers. They are given a novel temporally aware treatment in which operations’ specifications include their execution times and their continuations know that an operation’s worth of additional time has passed before they start executing, making it possible to safely access further temporal resources in them.
On the Role of Scorers in Curbing Examination Malpractices

Examination malpractices as a phenomenon cross the boundaries set by time and geography and are often an issue in Nepal as well generating a considerable amount of discourse amongst students, teachers, parents and all the concerned. In theory, subjective testing does allow scorers the room for the play of their personal judgement. Nonetheless, a thread of argument prevails in the Nepalese academic circle, the argument attempting to curtail the judgement of scorers stating that scorers are obliged to award scores for what answer sheets contain and that they lack any right to judge whether or not the examinee performances might be the result of one or the other kind of malpractices that are also the product of the dereliction of invigilators and other test administration staff responsible for overseeing the performance of examinees. This paper follows this argument and endeavors to supply evidence for reinforcing the counter-argument that stands equally strong – the argument advocating the scorer right to judge and treat any malpractices evident in examinee performances. This right is conceived as one of the effective measures to curb examinee malpractices. For this, it supplies the justification for the exercise of scorer right, if formally and explicitly conferred on scorers, to treat the instances of examinee malpractices that are evident in the answer sheets of examinees.
Sistem Deteksi Kecanduan Pornografi Berbasis Chatbot Menggunakan Pornography Addiction Screening Tool (PAST)

The use of the internet not only brings benefits but also harms the ease of access to pornographic content. Pornographic content is not solely available on adult websites but is also spread on social media accessed by many children to adults. As a result, problems arise due to pornography addiction, such as rape, sexual orientation deviation, and sexual crimes against children. Uniquely, pornography addiction is hard to detect objectively compared to drug or alcohol addiction. This study aims to develop a pornography addiction detection system in the form of a chatbot-based mobile application. The choice of chatbot is because this system supports interactive automatic communication patterns and guarantees the privacy of its users. This study adopts a psychological measurement technique Pornography Addiction Screening Tool (PAST), as a benchmark in developing application logic. Meanwhile, chatbot application development uses the waterfall method which has been tested by best practices as a software development method. Tests performed are in a developer environment. The system validation mechanism uses the Black-box testing method to observe the execution of the application when it runs the functionality that was designed beforehand. In addition, the usability measurement level application is adopted from the System Usability Scale (SUS) method. From the test results, it was found that the pornography addiction detection system operates normally and can be used as a supporting medium for handling pornography addiction by psychiatrists.
Prediksi Hasil Produksi Tanaman Tomat di Indonesia Menurut Provinsi Menggunakan Algoritma Fletcher-Reeves

Tomatoes are essential for Indonesians because they have high economic and nutritional value. In addition, as population growth increases, the demand for tomatoes also increases. Based on this, it is essential to research to predict the future development of tomato crop production. The research in this paper uses a dataset of tomato plant production in Indonesia, which is spread across 34 provinces in the last seven years, namely from 2015 to 2021), which is sourced from the Indonesian Central Bureau of Statistics and the District/City Agriculture Service of each Province. The algorithm proposed in this study is the Fletcher-Reeves Conjugate Gradient Algorithm which will be processed with the help of Matlab2011b. Research analysis with three network architectural models: 5-7-1, 5-13-1, and 5-17-1. Based on a network comparison of the three architectural models, the best result is the 5-17-1 model because the MSE value is the smallest compared to the other two models, namely 0.0009915 compared to 0.0010851 and 0.0049764, as well as the highest level of accuracy, namely by 94% versus 91% and 88%. Therefore the 5-17-1 model is used to predict the yield of tomato production in Indonesia for the future (2022 and 2023). Based on the prediction results at the end of 2022 and 2023, there are 18 provinces where tomato crop production has the potential to increase, although not too significantly. The prediction of tomato production using the Fletcher-Reeves algorithm is quite good because it produces a small error rate and high accuracy.
Deteksi Kesehatan Janin Menggunakan Decision Tree dan Feature Forward Selection

Fetal health is very important, it is important for prospective mothers to know from an early age, until now a mother’s sense of ignorance abpout fetal health is very lacking and can result in fetal death. This is due to the lack of curiosity possessed by prospective moyhers and the lack of socialization and infrastructure from related parties about fetal health. Basically, the growth and development of a prospective fetus is very important so that it can be born healthy and without any obstacles at all.The pupose of this study was to detect fetal health using a decision tree classification algorithm with forward feature selection. This experiment was conducted using a public dataset of 2.126 patient data. The results showes that classification using a decision tree algorithm using a decision tree algorithm without feature selection resulted in an accuracy of 89.84%. While the use of the forward selection feature in this decision tree algorithm produces an accuracy of 91.06%.This shows that the use of the forward selection feature can increase accuracy by 1.22%.
Hotel Selection Decision Support System with the Simple Additive Weighting (SAW) Method

Purwokerto as the city center in southwestern Central Java which is one of the tourism places in Central Java with a fairly large number of enthusiasts. Purwokerto City has a wide selection of tourist attraction destinations that can be visited by tourists. There are not a few tourists who come from outside the city and do tours for more than one day. If you look at these conditions, a temporary stopover place is needed, namely a hotel. Purwokerto City provides so many choices of hotels spread across various locations with lodging classes, rental prices, facilities and services that are diverse. With the existence of many and different hotel facilities, of course, visitors will find it difficult to find and determine a hotel that matches the desired criteria. In addition, they will also find it difficult in finding the location of the desired hotel. The calculation results using the SAW (Simple Additive Weighting) Method found that Java Heritage Hotel got a value of 0.9, Resort and Hotel Atrium by 0.77, Surya Yudha Hotel by 0.65 and Trisno Hotel by 0.55. And get the same result between manual calculations and calculations from the system.. Based on the description above, in this study a decision support system was developed that can help tourists to determine hotels according to the wishes and needs of tourists using the SAW Method (Simple Additive Weighting)
Automatic Software Defect Assignment Method Based on Deep Learning Processing Algorithm

Due to the maturity of information technology and the change of market demand, the software structure is more complex, the reports of software project defects are increasing, and the personnel and time costs of software project management are becoming more and more expensive. Therefore, more automatic and semi-automatic defect report dispatch algorithms are proposed to replace the original manual dispatch. This paper aims to study the automatic software defect assignment method based on deep learning processing algorithm. Based on the analysis of deep learning processing algorithm, software defect classification and defect report life cycle, an automatic defect assignment method based on deep learning is proposed. Experimental verification was carried out on the two large data sets of large open source software Eclipse and Moilla. The results show that the effect of adding two features at the same time is greatly improved.
Automatic Scoring of English Online Translation Based on Machine Learning Algorithm

In recent years, with the improvement of China's comprehensive national strength, people have higher and higher requirements for the quality of daily life, and English, as one of the indispensable languages for human communication, has become the most popular and commonly used tool for international cultural communication. It is of great significance to study the online efficient, accurate and fast translation methods based on machine learning algorithms(MLA). This paper first introduces the principle of English machine translation and expounds the scoring standard of online translation, and then studies the application of MLA. On this basis, an experimental test of automatic scoring of English online translation is carried out. Finally, the test results show that under the optimization of machine translation algorithm, the accuracy of English online translation is very high, which can reach more than 90%.
Augmented reality (AR)

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Research on MPPT Simulation of Photovoltaic Power Generation Based on Improved Genetic Algorithm

The mathematical model of solar photovoltaic power generation system is a typical two-dimensional mathematical model, which can be used in engineering applications. For example, it can be applied to power system, industrial automation design, microelectronics, control and other fields. At present, the mathematical model of photovoltaic power generation system has not been widely studied and applied. Therefore, the simulation calculation can better guide the operation status monitoring and analysis of photovoltaic power generation system. This paper takes photovoltaic power generation system as an example for simulation analysis. In order to verify the correctness and feasibility of the actual model, MPPT simulation program is used to simulate the photovoltaic power generation performance. The test results show that under 0.0001 load cycle, the working characteristics of the progressive conductivity method are basically the same as those of the incremental conductivity method. Under 0.0005 load step, the oscillation occurs at the maximum power point, but the maximum power tracking time is only 0.05 seconds.
Penerapan K-Means dan Fuzzy C-Means untuk Pengelompokan Data Kasus Covid-19 di Kabupaten Indragiri Hilir

In the beginning of 2020 world was shocked because new virus spreaded, that is Coronavirus Disease 2019 (Covid-19). This virus spread quickly in almost country, including Indonesia. Covid-19 virus deployment started in various regions in Indonesia stay increasing everyday. this research has been done the region clustering that infected Covid-19 case in Indragiri Hilir district to inform to central government about Covid-19 handling. To do Clustering in this research used K-Means and Fuzzy C-Means Algorithm. After done some of test, it's obtained the ratio which was tested with Silhouette Index and Partition Coefficient, SI validity value of K-Means is 0,950 while PCI validity value of Fuzzy C-Means is 0,960. The results have been obtained shown that Fuzzy C-Means Method is the best Method to do Clustering Covid-19 data in Indragiri Hilir district Because the validity value is closed to 1 which is located in K=3.
Penerapan Metode Dempster Shafer Dalam Mendiagnosa Penyakit Tumor Hipofisis

Pituitary tumors are relatively common intracellular neoplasms, and constitute 10-15% of all intracial neoplasms. These types of tumors often have difficulties and not infrequent recurrence, even though surgery has been done. Although there have been many studies on pituitary tumors, the pathogenesis of the occurrence of these tumors is not entirely clear. It is generally considered that pituitary neoplasms are primary pituitary tumors. Biomolecular electrons show that pituitary tumors, both functional and non-functional, originate from the growth of one clone (monoclonal). The Dempster-Shafer method is used to look for inconsistencies in effects and also new facts that will change existing attitudes, using the Dempster-Shafer method that allows a person to be safe in the work of an expert. This study aims to apply the Dempster-Shafer method to expert systems to diagnose the risk level of a person's pituitary tumor based on factors and symptoms of a pituitary tumor. The problem experienced by the community is the difficulty of specialist pituitary tumors due to the limited time of specialists to work, besides that in terms of very expensive costs for them or those whose economies cannot diagnose or consult with tumor specialists.
Generative Adversarial Network (GAN) to Generate Realistic Images

Abstract: Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results. Image synthesis is among the most thoroughly researched applications of GANs, and the results thus far have demonstrated the potential of GANs in image synthesis. This paper provides a taxonomy of the methods used in image synthesis, reviews various models for text-to-image synthesis and image-to-image translation, discusses evaluation metrics, and highlights future research directions for image synthesis using GANs..
Region-Enhanced Feature Learning for Scene Semantic Segmentation

Semantic segmentation in complex scenes relies not only on object appearance but also on object location and the surrounding environment. Nonetheless, it is difficult to model long-range context in the format of pairwise point correlations due to the huge computational cost for large-scale point clouds. In this paper, we propose using regions as the intermediate representation of point clouds instead of fine-grained points or voxels to reduce the computational burden. We introduce a novel Region-Enhanced Feature Learning Network (REFL-Net) that leverages region correlations to enhance point feature learning. We design a region-based feature enhancement (RFE) module, which consists of a Semantic-Spatial Region Extraction stage and a Region Dependency Modeling stage. In the first stage, the input points are grouped into a set of regions based on their semantic and spatial proximity. In the second stage, we explore inter-region semantic and spatial relationships by employing a self-attention block on region features and then fuse point features with the region features to obtain more discriminative representations. Our proposed RFE module is plug-and-play and can be integrated with common semantic segmentation backbones. We conduct extensive experiments on ScanNetV2 and S3DIS datasets and evaluate our RFE module with different segmentation backbones. Our REFL-Net achieves 1.8% mIoU gain on ScanNetV2 and 1.7% mIoU gain on S3DIS with negligible computational cost compared with backbone models. Both quantitative and qualitative results show the powerful long-range context modeling ability and strong generalization ability of our REFL-Net.
A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenges. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation study to demonstrate the effectiveness of our proposed method.
Tractable Control for Autoregressive Language Generation

Despite the success of autoregressive large language models in text generation, it remains a major challenge to generate text that satisfies complex constraints: sampling from the conditional distribution ${\Pr}(\text{text} | \alpha)$ is intractable for even the simplest lexical constraints $\alpha$. To overcome this challenge, we propose to use tractable probabilistic models (TPMs) to impose lexical constraints in autoregressive text generation models, which we refer to as GeLaTo (Generating Language with Tractable Constraints). To demonstrate the effectiveness of this framework, we use distilled hidden Markov models, where we can efficiently compute ${\Pr}(\text{text} | \alpha)$, to guide autoregressive generation from GPT2. GeLaTo achieves state-of-the-art performance on challenging benchmarks for constrained text generation (e.g., CommonGen), beating various strong baselines by a large margin. Our work not only opens up new avenues for controlling large language models but also motivates the development of more expressive TPMs.