Reviewerly/exHarmony huggingface dataset:
Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article

The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment value of neutral 2056, positive 1200, and negative 1292. In addition, Naïve Bayes gets the highest accuracy score of 95, 45%.&#x0D;
Reconstructing words using queries on subwords or factors

We study word reconstruction problems. Improving a previous result by P. Fleischmann, M. Lejeune, F. Manea, D. Nowotka and M. Rigo, we prove that, for any unknown word $w$ of length $n$ over an alphabet of cardinality $k$, $w$ can be reconstructed from the number of occurrences as subwords (or scattered factors) of $O(k^2\sqrt{n\log_2(n)})$ words. Two previous upper bounds obtained by S. S. Skiena and G. Sundaram are also slightly improved: one when considering information on the existence of subwords instead of on the numbers of their occurrences, and, the other when considering information on the existence of factors.
Visual attention learning and antiocclusion-based correlation filter for visual object tracking

Discriminative correlation filter-based (DCF) object-tracking methods exhibit excellent and efficient performance in short-term tracking. However, these methods do not evaluate the credibility of the feature channels and spatial features. In addition, they regard the candidates with the largest response in each frame as the training sample without evaluating the confidence of these samples. As a result, DCF may be misled by distractors and fails in the occlusion scene. We propose a visual attention learning and anti-occlusion-based correlation filter tracking method to solve the abovementioned limitations. First, we propose a correlation filtering framework for simultaneous estimation of position and scale in which we sample the multi-scale detection candidates from the target’s center in the previous frame and calculate the spatial attention of each candidate according to the spatial prior and color likelihood. Then, we design a channel attention calculation algorithm to evaluate the importance of each channel according to the response in the learning and detection stages. Finally, a historical multi-template pool is proposed to further evaluate the confidence of the best candidate in each frame, achieving robust tracking in occlusion scenarios. A series of experiments are conducted to verify the proposed method, indicating that the proposed method achieves excellent results on the OTB50, OTB100, and UAV20L datasets and outperforms many state-of-the-art methods.
Implementasi Model Soil dan Water Assessment Tool (SWAT) Dalam Pengelolaan Das Mikro Sei Tanduk

The Soil and Water Assessment Tool (SWAT) model which is integrated with the Geographic Information System (GIS) can provide an overview or prediction of the influence of land management on the hydrology of the Micro Sei Tanduk watershed in accordance with the interests of evaluating and monitoring watershed management activities. The purpose of this study was to analyze the management of the Sei Tanduk Micro watershed based on land use patterns, as a basis for determining the best recommendations for planning and evaluating the Indragiri Rokan Watershed Management Project Activities for Fiscal Year 2003 by compiling a Master Plan for the Micro Sei Tanduk Watershed Master Plan Rumbio Village, Kampar Regency. The research method is to process primary and secondary data using a SWAT model that is integrated with GIS in analyzing hydrological characteristics in land use scenarios in the management of the Micro Sei Tanduk watershed, as a basis for providing recommendations. The best recommendation from the evaluation and analysis of the Micro Sei Tanduk watershed management based on land use patterns is scenario 2 in the form of applying agroforestry on mixed dry land agriculture with the insertion of forestry trees based on community participation in the Micro Sei Tanduk watershed, Rubio Village, Kampar District, Kampar Regency.
Improving the Pair Selection and the Model Fusion Steps of Satellite Multi-View Stereo Pipelines

Multi-view stereo reconstruction of scenes from satellite images is traditionally performed with a pair-wise stereovision approach: (1) multiple views are grouped into pairs, (2) each pair is processed by two-view stereo methods producing an elevation model or point cloud, lastly (3) the pairwise reconstructions are integrated and filtered to obtain a final result. These steps are organized in a pipeline and the end-to-end performance of reconstructions depends on the behavior of these steps. This work introduces two changes that increase the performance of the reconstructions: a new pair selection approach and a new integration method are presented. The new pair selection replaces commonly used heuristics with a principled criterion that predicts the completeness of a pair based on offline simulations. The presented integration method is based on an iterated bilateral filter. Experiments show that these changes yield a systematic improvement on the performance of the pipeline.
Large-Scale Open-Set Classification Protocols for ImageNet

Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while rejecting previously unseen unknown samples. Only recently, research started to investigate on algorithms that are able to handle these unknown samples correctly. Some of these approaches address OSC by including into the training set negative samples that a classifier learns to reject, expecting that these data increase the robustness of the classifier on unknown classes. Most of these approaches are evaluated on small-scale and low-resolution image datasets like MNIST, SVHN or CIFAR, which makes it difficult to assess their applicability to the real world, and to compare them among each other. We propose three open-set protocols that provide rich datasets of natural images with different levels of similarity between known and unknown classes. The protocols consist of subsets of ImageNet classes selected to provide training and testing data closer to real-world scenarios. Additionally, we propose a new validation metric that can be employed to assess whether the training of deep learning models addresses both the classification of known samples and the rejection of unknown samples. We use the protocols to compare the performance of two baseline open-set algorithms to the standard SoftMax baseline and find that the algorithms work well on negative samples that have been seen during training, and partially on out-of-distribution detection tasks, but drop performance in the presence of samples from previously unseen unknown classes.
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization

We focus on controllable disentangled representation learning (C-Dis-RL), where users can control the partition of the disentangled latent space to factorize dataset attributes (concepts) for downstream tasks. Two general problems remain under-explored in current methods: (1) They lack comprehensive disentanglement constraints, especially missing the minimization of mutual information between different attributes across latent and observation domains. (2) They lack convexity constraints, which is important for meaningfully manipulating specific attributes for downstream tasks. To encourage both comprehensive C-Dis-RL and convexity simultaneously, we propose a simple yet efficient method: Controllable Interpolation Regularization (CIR), which creates a positive loop where disentanglement and convexity can help each other. Specifically, we conduct controlled interpolation in latent space during training, and we reuse the encoder to help form a ’perfect disentanglement’ regularization. In that case, (a) disentanglement loss implicitly enlarges the potential understandable distribution to encourage convexity; (b) convexity can in turn improve robust and precise disentanglement. CIR is a general module and we merge CIR with three different algorithms: ELEGANT, I2I-Dis, and GZS-Net to show the compatibility and effectiveness. Qualitative and quantitative experiments show improvement in C-Dis-RL and latent convexity by CIR. This further improves downstream tasks: controllable image synthesis, cross-modality image translation and zero-shot synthesis.
Convergence of Music and Architecture: the works of Iannis Xenakis, György Ligeti, CCY Architects

Composers gain structural ideas from their original style of composition for Music and other areas. This tendency has been conspicuous since the twentieth century, and various fields of interdisciplinary studies have been conducted recently: Typical examples of multidisciplinary studies include the integration of Music and architecture, Music and Science, Music and mathematics, and music and fine arts. Music has expanded the horizon to unattempted areas by developing unconventional ideas and creative composition techniques.&#x0D; This study investigated three works: two for composition and one for architecture. The orchestral piece, Metastasis by Iannis Xenakis, piano work, Columna Infinita by György Ligeti and the architecture Music Box, located in Aspen, Colorado, the United States are significantly studied in the paper to explore composers and architects’intentions, structural manners and the background of the works.&#x0D; Research implications suggest integrating independent theories in different fields of study to create a new multidisciplinary field. We can expand traditional boundaries in academic areas without limitations, and research on interdisciplinary studies needs to be carried out further. It will inspire and drive researchers in numerous fields who want to try something new and creative.
Swarm Intelligence and Its Impact on Data Mining and Knowledge Discovery

The swarm intelligence algorithms are established as comprehensive approaches for resolving the complicated problems in optimization through the simulation of “behaviors of the biological swarms.” Recently, data mining areas seek more attention that requires analysis of huge data and quick management. Most of the traditional techniques have not provided effective performance, and they are only applicable for differentiable and continuous functions. Further, the group of population-based techniques is proved to be effective in certain researches, where the swarm intelligence algorithms provide the significant potential for relevant data mining tasks. Hence, this paper demonstrates the biological inspiration and some of the swarm intelligence concepts, in which the main focus is given to “Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms.” The primary data mining strategies are also described along with certain existing and present works based on swarm intelligence approaches.
Novel Framework to Define Extended &amp; Mixed Reality for Online Learning

COVID-19 has taken educational protocols to different track. Online education is the way to execute process smoothly. There are various tools that are providing services to fill the pain points. Role of emerging technologies can't be denied. Extended and mixed reality plays a vital role here. Our purpose is to analyze the same in this paper. Second objective of this paper is to develop novel framework to define extended and mixed reality for online learning Fig.~\\\\\\\\ref{fig1}. Testing has been done on four Indian universities and schools to identify the effectiveness of proposed framework.
An Experimental Study on Hybrid Feature Selection Techniques for Sentiment Classification

Text sentiment classification aims to extract useful information from unstructured text data and classify its sentiment into positive and negative categories. Irrelevant features and high-dimensional feature space from text data are common issues in sentiment classification because they degrade the classification performance. To address these issues, this study applies hybrid feature selection using Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to three text datasets: IMDB, Yelp, and Amazon. The TF-IDF is employed to select sentiment features, which are further refined by SVM-RFE. Finally, SVM is applied to determine whether the sentiment is positive or negative. This study outperforms the existing techniques in two datasets: 88% accuracy in the IMDB dataset and 84.5% in the Yelp dataset. Meanwhile, the accuracy in the Amazon dataset is lower than the existing studies, at 81.5%. These results indicate inconsistency of the technique, and it opens the opportunity for further research on the other hybrid feature selection techniques for sentiment classification to improve the accuracy in all datasets. Also, the results show that the technique improved classification performance and reduced feature space by 63%.
Online Handwritten Recognition For Alphabet Writing Practice

Learning to write in children requires the child's habit of using a pencil to write on paper. The manual method has drawbacks such as the use of a lot of paper when children have to study harder to keep repeating the exercises and also cannot be done online remotely. In addition to this, writing exercises on the touchscreen can also train children to be more skilled at using the touchscreen. To overcome these problems, the author will discuss the use of handwriting recognition technology to practice writing capital letters (A-H), where the technology will be built on a web-based basis. The recognition algorithm used is K-NN. Testing the use of the website was carried out at Tunas Mekar Sari Kindergarten, Denpasar. The identification number and result will be stored on the web server. Based on the experiment, it was found that from 80 image data, the K-NN algorithm succeeded in providing accurate recognition results on 60 image data, and obtained an accuracy of 78.75%.
Application Of Intelligent Construction In Architectural Decoration Industry

In the context of the continuous growth of the market space of the architectural decoration industry and the continuous decline of the employed population, the architectural decoration industry is bound to develop in the direction of intelligent construction. Taking the Xinyi Garden C area project and the Panda Tower project as examples, this paper analyzes the application of intelligent construction in the architectural decoration industry from two parts: fabricated interior decoration and complex facade decoration. The technical system of fabricated interior wall, floor, pipeline, toilet system and the realization method of intelligent construction of complex facade are discussed, which provides a reference for similar projects in the later period.
Prediction of Students Final Project Values Based on Errors in Scientific Writing Using Data Mining Classification Algorithms

College databases are quickly amassing more and more data. Some data, which has not been used to raise the caliber of student performance, includes secret information regarding student performance. Data mining is used to examine accessible educational data and reveal any hidden information that may be there. This study uses a data mining classification model to develop rules that can forecast the final project value of program students' information management based on course values that aid in the final project's preparation. The study's analysis of student performance in courses that help them prepare for their final projects will also be included. According to the subject that underpins their final project, this prediction is supposed to assist in determining grades. Predictive analysis employing ID3 has an accuracy of 65.54%, CHAID 70.67%, and Nave Bayes 73.13%, According to research that has been done, the competition from the Naive Bayes algorithm has succeeded in being slightly superior to other algorithms.
A Fuzzy Clustering Based Approach for Heavy Tail Weight in Web Data

A distributed frameworks cluster is a gathering of machines that are for all intents and purposes or topographically isolated and that cooperate to give a similar services or application to customers in web application. It is conceivable that huge numbers of the services you keep running in your system today are a piece of a distributed system cluster. Cluster is a vital information mining procedure which expects to isolate the information objects into important gatherings called as groups. It is the way toward gathering objects into bunches to such an extent that items from a similar group are comparable and objects from various groups is unique. In information mining, information bunching has been examined for long time utilizing diverse calculations and ordinary patterns are proposed for better results around tailed data. The fuzzy semantic strategy is look at to group the overwhelming followed information by utilizing some technique for remove. An appraisal thinks about is introduced in view of time and exactness. In this method proposed here is evaluated to other relational clustering schemes using various propinquity matrices as input. Simulations demonstrate the scheme to be very effectual.
Current Status and Prospects of Popularization of ShiJi

Only when humanities constantly communicate with modern readers and undergo new changes in accordance with reality can they maintain their vitality. There fore, the popularization of humanities is not just a temporary movement, but should be constantly explored to set the right direction. From this perspective, the author tries to review how the popularization of "ShiJi" is carried out, taking the publications from 2010 to 2021 as the research object, content and characteristics. Over the past 12 years, 177 publications have been published, including 75 original translations (43 percent), 55 (31 percent) for ordinary adults, 36 (20 percent) for children, and 11 (6 percent) for teenagers.From the point of view of popularization, these publications have a strong academic nature centered on the translation of the original. The emphasis on conveying the meaning of the original “ShiJi” is somewhat inadequate, taking into account both the variations and applications of reality.To solve this problem, publications need to improve the readability of Historical Records, differentiate the readership and consider its specific application.Considering the changes in reality and the preferences of the MZ generation, it is necessary to change the way of reading a single paper edition of “ShiJi”.
Examples of the University"s Community Engaged Curriculum Operation Using Living Lab :Focusing on Application Cases of "A" Liberal Arts Classes in University Technical Education

본 연구는 새로운 교육혁신 플랫폼인 대학 리빙랩(Living Lab)으로 지역사회 혁신 교과목을 운영하고, 그 실제 운영사례의 분석에서 시작되었다. 본 연구의 목적은 대학과 지역사회가 연계한 협업의 가능성을 확보하려는 것이다. 따라서 본 논문은 2022학년도 1학기에 C대학교 기술교육과에서 개설된 교양 수업인 ‘창의 발명과 팀 문제해결’ 교과목에서 리빙랩을 적용하였다. 수업은 총 15주로 진행되었으며, 가장 핵심이 되는 활동은 지역사회의 의제를 발굴하여 기획리빙랩으로 문제 해결 방안을 도출하는 팀별 프로젝트 활동으로 12주간 운영하였다. 대학교육에서 지역사회와 연계하여 리빙랩(Living Lab) 교과목을 운영한 연구 결과 및 내용은 다음과 같다. 첫째, 지역사회와 연계한 교과목의 운영은 대학 교육의 현장지향성 향상과 지역사회와의 협력 관계 형성에 도움이 되었다. 둘째, 리빙랩 교과목 운영은 교육 및 연구혁신의 새로운 모델을 만들어낼 수 있었다. 셋째, 리빙랩을 통해 대학생 중심의 지역사회 혁신의 기틀을 마련할 수 있었다. 대학에서 리빙랩이 활성화되기 위한 정책적 과제는 다음과 같다. 첫째, 대학에서 교과 운영의 협력적 거버넌스를 구축해야 한다. 둘째, 리빙랩 운영 교과를 확장해야 한다. 셋째, 지역사회와의 커뮤니티 플랫폼 형성과 활성화가 반드시 필요하다. 넷째, 리빙랩이 대학교육에서 활성화되기 위해서는 최종결과물에 대한 지속적인 관리체계가 필요하다. 향후 이러한 교과목 운영사례들이 많이 제시되어 대학교육에서 리빙랩이 지속가능한 운영이 되기를 기대한다. This study began with the analysis of actual cases of operating community innovation subjects as a new educational innovation platform, University Living Lab. The purpose of this study is to secure the possibility of collaboration between universities and local communities. To this end, the Living Lab was applied in the ‘Creative Invention and Team Problem Solving’ subject, a liberal arts class opened in the Technical Education Department of C University in the first semester of the 2022 academic year. Classes were held for a total of 15 weeks, and the most important activity was a project activity for each team that discovered the local community"s agenda and came up with a problem-solving method through the Planning Living Lab, which was operated for 12 weeks. The achievements of operating Living Lab subjects in connection with the local community in university education are as follows. First, the operation of subjects in connection with the local community helped improve the field orientation of university education and formed a cooperative relationship with the local community. Second, the operation of Living Lab subjects could create a new model for education and research innovation. Third, through the Living Lab, it was possible to lay the groundwork for university student-centered community innovation. The policy tasks for revitalizing living labs in universities are as follows. First, it is necessary to establish cooperative governance of course operation in universities. Second, living lab operation subjects should be expanded. Third, it is absolutely necessary to form and activate a community platform with the local community. Fourth, in order for Living Lab to be vitalized in university education, a continuous management system for final results is needed. In the future, it is hoped that many cases of operating these subjects will be presented, and that Living Lab will become a sustainable operation in university education.