Reviewerly/exHarmony huggingface dataset:
Knowledge Graph Bidirectional Interaction Graph Convolutional Network for Recommendation

Recently, the recommended method based on the Knowledge Graph (KG) has become a hot research topic in modern recommendation systems. Most researchers use assistive information such as entity attributes in KG to improve recommendation performance and alleviate Collaborative Filtering (CF) sparsity and cold start problems. The most recent technical trend is to develop end-to-end models based on the Graph Convolutional Network (GCN). In this paper, we propose a Knowledge Graph Bidirectional Interaction Graph Convolution Network for recommendation (KBGCN). This method is used to refine the embedded representation of node by recursively delivering messages from the neighbors (attributes or items) of the node (entity) and applies the knowledge aware attention mechanism to distinguish the contributions of different neighbors based of the same node. It uses neighbors of each entity in KG as the view of this entity, which can be extended by expanding the view of Multi-hop neighbors to mine high-order connectivity information existing in KG automatically. We apply the proposed method to three real-world datasets. KBGCN is better than seven KG-based baselines in recommendation accuracy and the two state-of-the-art GCN-based recommendations frameworks.
Fast Re-Optimization of LeadingOnes with Frequent Changes

In real-world optimization scenarios, the problem instance that we are asked to solve may change during the optimization process, e.g., when new information becomes available or when the environmental conditions change. In such situations, one could hope to achieve reasonable performance by continuing the search from the best solution found for the original problem. Likewise, one may hope that when solving several problem instances that are similar to each other, it can be beneficial to “warm-start” the optimization process of the second instance by the best solution found for the first. However, it was shown in [Doerr et al., GECCO 2019] that even when initialized with structurally good solutions, evolutionary algorithms can have a tendency to replace these good solutions by structurally worse ones, resulting in optimization times that have no advantage over the same algorithms started from scratch. Doerr et al. also proposed a diversity mechanism to overcome this problem. Their approach balances greedy search around a best-so-far solution for the current problem with search in the neighborhood around the best-found solution for the previous instance. In this work, we first show that the re-optimization approach suggested by Doerr et al. reaches a limit when the problem instances are prone to more frequent changes. More precisely, we show that they get stuck on the dynamic LeadingOnes problem in which the target string changes periodically. We then propose a modification of their algorithm which interpolates between greedy search around the previous-best and the current-best solution. We empirically evaluate our smoothed re-optimization algorithm on LeadingOnes instances with various frequencies of change and with different perturbation factors and show that it outperforms both a fully restarted ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1+1$</tex> ) Evolutionary Algorithm and the re-optimization approach by Doerr et al.
A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics

It is no secret that optimisation is a popular topic in any practical engineering application. Similarly, Metaheuristics (MHs) are a fairly standard approach for solving optimisation problems due to their success, flexibility, and simplicity. However, it is seldom easy to find a solver from the overpopulation of metaheuristics that adequately deals with a given problem. For that reason, the solver selection is even considered an additional problem in many optimisation scenarios. This work investigates the Metaheuristic Composition Optimisation Problem, which involves designing heuristic-based procedures that solve continuous optimisation problems. Therefore, we propose two novel and still simple methodologies based on transfer learning to facilitate the automatic generation of population-based and metaphor-less MHs by using search operators from the literature. To represent these solvers, we adopt our previously proposed unfolded MH model. The first strategy deals with the problem dynamically, building the sequence while solving the low-level problem. In contrast, the second one does it statically by generating the whole candidate sequence before implementing it. Results provide us with information to prove the feasibility of these approaches via experiments using 32 problems with four different characteristic groups and four dimensionalities and varying the number of agents (30, 50, and 100) employed by the search operators. We also remark that one can compare these two methodologies on performance, but we emphasise their potential usage depending on the general application environment.
Construction of Multilevel Periodic Complementary Pairs over Integers

A multilevel periodic complementary pair (PCP) over integers is a pair of integer sequences with the optimal sum of periodic autocorrelation functions that can be used to augment the known Golay complementary pairs (GCPs). Multilevel PCPs have the advantage of being arbitrary in length when compared to GCPs. As a result, this study proposes two construction options. The first is to construct multilevel PCPs of any length with an uncertain degree using perfect Gaussian integer sequences, and the second is to construct multilevel PCPs of odd prime length with a specified degree based on cyclotomic classes; addionally, the length of PCPs can be extended by interleaving with perfect sequences. A huge number of multilevel PCPs can be obtained using computer search.
MACHINE LEARNING APPROACH FOR EARLY DETECTION OF ALZHEIMER'S DISEASE

Alzheimer's Disease (AD) is thought to be the most generally known cause of dementia, and it is estimated that only one in four people with Alzheimer's are properly identified.While there is no definitive cure, the negative effects may be ignored while the weakening is mild.Treatment is most effective when it begins before major downstream injury occurs, i.e., at the stage of moderate cognitive impairment (MCI) or much earlier.Physical and neurological examinations and neuropsychological and cognitive testing are used to diagnose Alzheimer's disease.There is a need for improved diagnostic tools, which is what this postulation addresses.Kaggle is an online open-access dataset for improving the Alzheimer's disease diagnosis technique.The information acquired during the conference is documented.One goal of this theory's research is to look at machine learning methodologies to create a classifier that can help screen new persons for different stages of Alzheimer's disease.In comparison to previous work methods, our methodology is suitable for breaking down diverse classes in a single setting and needs less distinct training samples and inconsequential prior knowledge.In our tests, we saw a significant improvement in categorizing all diagnostic categories.Initially, the model is trained on 64 evaluated examples from the Kaggle database.We next test our created model on the whole set of entries supplied by the Kaggle dataset to confirm our framework's finding.Our results show 96.37% accuracy in AD detection and categorization.
Solving In-door Human Activity Recognition via RFID based on Unsupervised Domain Adaptation

With the changes of the times and the rise of technology, artificial intelligence and radio frequency identification technology (RFID) have become an important part of indoor fields such as health care, smart home, etc., which makes indoor human activity recognition based on RFID and artificial intelligence technology a hot research topic. Due to the complexity and diversity of indoor scenes, there will be large deviations in the recognition of behaviors and actions in different indoor scenes. Based on the above considerations, this paper combines the idea of unsupervised domain adaptation, and uses technologies such as KNN, SVM and FCN to establish a human activity recognition model, thereby improving the accuracy of activity recognition. After applying unsupervised domain adaptation, the performance of all models is greatly improved, among which, the accuracy of the SVM model reaches 90.8% and the F1score reaches 90.4%. With the help of this model, user behavior can be quickly and accurately identified in indoor scenarios such as nursing homes and rehabilitation wards, providing effective security for the daily activities of vulnerable groups such as the elderly and patients, and has broad industry application prospects.
Similarity-Guided and Multi-Layer Fusion Network for Few-shot Semantic Segmentation

Few-shot semantic segmentation aims to tackle the problem that segmenting unseen object class using only a few support images with the same object class. At present, most related methods focus on prototype learning or feature similarity. However, these few-shot segmentation methods do not make good use of high-level features to enhance the prediction results. In this paper, we propose a lightweight Similarity-Guided and Multi-layer Fusion Network (SMNet) with two modules including Similarity-Guided Module (SGM) and Multi-Layer Fusion Module (MLFM). Specifically, the SGM utilizes cosine similarities in multiple high-level feature layers to augment the features in middle-level from query and support image, and then augmented features are refined via a residual attention module. In order to enhance the diversity of features, we reformulate the refined features as a spatiotemporal sequence problem. Then, we introduce the MLFM, which combines two ConvLSTMs to obtain fused feature from different scales. Finally, the decoder takes fused features to obtain predicted mask. Experiment results demonstrate that our model can achieve superior or competitive performances in several datasets.
QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach

This work explains why and how QoS modeling has been used within a multicriteria optimization approach. The parameters and metrics defined are intended to provide a broader and, at the same time, more precise analysis of the issues highlighted in the work dedicated to placement algorithms in the cloud. In order to find the optimal solution to a placement problem which is impractical in polynomial time, as in more particular cases, meta-heuristics more or less approaching the optimal solution are used in order to obtain a satisfactory solution. First, a model by a genetic algorithm is proposed. This genetic algorithm dedicated to the problem of placing virtual machines in the cloud has been implemented in two different versions. The former only considers elementary services, while the latter uses compound services. These two versions of the genetic algorithm are presented, and also, two greedy algorithms, round-robin and best-fit sorted, were used in order to allow a comparison with the genetic algorithm. The characteristics of these two algorithms are presented.
Visitor Prediction Decision Support System at Dieng Tourism Objects Using the K-Nearest Neighbor Method

A tourist target is anything that attracts a visitor or tourist to come to visit a place or area. Tourism goods play an important role in a country or region, becoming a source of national foreign exchange, increasing human resources, and improving the economy of surrounding communities. The problem posed in this study is how to implement a decision support system in predicting visitor numbers for Dieng tourists using the k-nearest neighbor method. The purpose of this study is to help the local government and surrounding communities to improve facilities such as restaurants, places of worship, parking lots, clean toilets so that tourists can feel safe and comfortable when visiting Dieng. Helps manage tourism targets. is what you give. These attractions using a decision support system as a process to predict visitors. The number of visitors who visited in December 2017 was 421,394, which serves as a reference for predicting the number of visitors who will visit Dieng in the following year. The predicted result is 29569.25 visitors with a parameter value of k = 8 and a minimum RMSE value of k = 1/0.
Sentiment Analyst on Twitter Using the K-Nearest Neighbors (KNN) Algorithm Against Covid-19 Vaccination

The corona virus (2019-nCoV), commonly known as COVID-19 has been officially designated as a global pandemic by the WHO. Twitter, is one of the social media used by many people and is popular among internet users in expressing opinions. One of the problems related to Covid-19 and causing a stir is the procurement of the Covid-19 vaccine. The procurement of the vaccine caused various opinions in Indonesian society, where the uproar was also quite busy being discussed on Twitter and even became a Trending Topic. The opinions that appear on Twitter will then be used as data for the Sentiment Analysis process. One of the members of the House of Representatives (DPR), namely RibkaTjiptaning was also included in the Trending Topic list on Twitter for refusing to receive the Covid-19 vaccine. Sentiment analysis itself is a computational study of opinions, sentiments and emotions expressed textually. Sentiment analysis is also a technique to extract information in the form of a person's attitude towards an issue or event by classifying the polarity of a text. Research related to Sentiment Analysis will be examined by dividing public opinion on Twitter social media into positive and negative sentiments, and using the K-Nearest Neighbor (KNN) algorithm to classify public opinion about COVID-19 vaccination. In the testing section, the Confusion Matrix method is used which then results in an accuracy of 85%, precision of 100%, and recall of 78.94%.
Corpus Annotation and Analysis of Sarcasm in Twitter: #CatsMovie vs. #TheRiseOfSkywalker

Sentiment analysis is a natural language processing task that has received increased attention in the last decade due to the vast amount of opinionated data on social media platforms such as Twitter. Although the methodologies employed have grown in number and sophistication, analysing irony and sarcasm still poses a severe problem. From the linguistic perspective, sarcasm has been studied in discourse analysis from several perspectives, but little attention has been given to specific metrics that measure its relevance. In this paper we describe the creation of a manually-annotated dataset where detailed text markers are included. This dataset is a sample from a larger corpus of tweets (n= 76,764) on two highly controversial films: Cats and Star Wars: The Rise of Skywalker. We took two different samples for each film, one before and one after their release, to compare reception and presence of sarcasm. We then used a sentiment analysis tool to measure the impact of sarcasm in polarity detection and then manually classified the mechanisms of sarcasm generation. The resulting corpus will be useful for machine learning approaches to sarcasm detection as well as discourse analysis studies on irony and sarcasm.
A characterization of abelian group codes in terms of their parameters

In 1979, Miller proved that for a group $G$ of odd order, two minimal group codes in $\mathbb{F}_2G$ are $G$-equivalent if and only they have identical weight distribution. In 2014, Ferraz-Guerreiro-Polcino Milies disprove Miller's result by giving an example of two non-$G$-equivalent minimal codes with identical weight distribution. In this paper, we give a characterization of finite abelian groups so that over a specific set of group codes, equality of important parameters of two codes implies the $G$-equivalence of these two codes. As a corollary, we prove that two minimal codes with the same weight distribution are $G$-equivalent if and only if for each prime divisor $p$ of $|G|$, the Sylow $p$-subgroup of $G$ is homocyclic.
E-Learning Materials in Earth and Life Science for Struggling Learners

The purpose of the study is to design and validate e-learning materials for a few selected Earth and Life Science disciplines.It concentrated on figuring out how the e-learning content would help increase the learners' conceptual comprehension and their perception of the intervention material's effectiveness.The area of investigation was limited to four (4) areas of investigation: 1) Determine the level of quality of the respondents on the use of the E -Learning Material in Earth and Life Science for Struggling Learners in terms of its objective, content and exercises, 2) Determine the level of acceptability of the respondents on the use of the E -Learning Material in Earth and Life Science for Struggling Learners in terms of its efficiency, usability and adaptability, 3) Know the mean score performance of the control and experimental group on the second quarter topic: matter in the use of E -Learning Material in Earth and Life Science in terms of Pre -Test and Post -Test, and 4) Determine if there is a significant difference between the pre-test performance and posttest performance of the control group and experimental group.It was founded on the level quality in terms of objectives, content, and exercises, it was determined that the elearning material was highly acceptable to teachers and students as an intervention for the struggling learners.The elearning material was successful in enhancing the students' competencies in Earth and Life Science disciplines as evidenced by the students' improved performance on the post-test.It was revealed that the e-learning material was highly acceptable to teachers and students as an intervention for the struggling learners in terms of acceptability based on its efficiency, usability and adaptability.According to the students' increased performance on the post-test, the e-learning material was effective in improving the students' competences in Earth and Life Science subjects.It was described that the mean score performance in the pretest of the control and experimental group were both average.While the mean score performance in the posttest of the control and experimental group were described as good.A significant improvement in the students' performance was revealed by the study.Based on the results of the pre-test and the post-test, there was a significant difference in the students' performance of the control and experimental group.Based from the findings of the study the author of this research concluded that there is a significant difference between the posttest mean score of the control and experimental group, hence, the null hypothesis stating that there is no significant difference is hereby rejected.The researcher recommends to change the research paradigm.Investigate the potential modifying impact of learners' academic abilities and parental support on the impact of employing science-related films as a motivating and cognitive teaching strategy during a certain grading period.
Reconstruction of historical suspended particulate matter contributions of Rhône River tributaries to the Mediterranean Sea

Total metal concentrations are usually used as geochemical tracers to identify sources of suspended particulate matter (SPM) and sediment deposits, but their non-conservative behaviour can lead to large uncertainties. This non-conservatism may be a significant issue when total metal concentrations of contemporary SPM are used to determine historical sediment sources, as diagenesis processes and/or past anthropogenic inputs modify metal concentrations in sediment core profiles. For the Rhône River, which is the main supplier of SPM to the Mediterranean Sea, there is no information on historical SPM source contributions from its main tributaries during major flooding events. The objective of this study was to highlight the relevance of using tracers in the non-reactive fraction to estimate the historical relative SPM contributions of the main tributaries of the Rhône River. These historical SPM contributions (1981–2017) were reconstructed based on a sediment core sampled at the outlet of the river basin and on sampling of contemporary SPM (2011–2019, Rhône Sediment Observatory monitoring network) from each main tributary. Fine stratigraphic resolution enabled us, using this original fingerprinting method, to reconstruct historical tributary contributions to sediment deposits at the scale of past flooding events over the last 37 years. When using tracers in the total fraction of SPM/sediments, the Isère and Ardèche rivers were the main contributors of SPM (32 % and 22 % on average, respectively) during the 1981–2017 period, whereas when using tracers in the non-reactive fraction, the main SPM source was the Durance River (52 %). The results obtained by this fingerprinting approach using the non-reactive fraction are fully consistent with literature data estimated using other methods (e.g. mean interannual SPM contribution of 53 % for the Durance River), and with documented historical flood events. This study showed that using tracers in the non-reactive fraction is more reliable than using tracers in the total fraction, especially for the deepest layers of the sediment core that are influenced by historical anthropogenic inputs.
Jaringan Syaraf Tiruan dengan Algoritma Backpropagation dalam Memprediksi Hasil Asesmen Nasional Berbasis Komputer (ANBK) SMP Se Kota Sawahlunto

Abstract&#x0D; The National Computer-Based Assessment for SMP level is a quality assessment program for all SMP level schools. This has only been simulated in 2019 and in 2021 this is the first stage of testing. Adaptation to ANBK needs to be done quickly so that the School Quality Score becomes good from time to time and the main goal of the education unit, namely the development of student competence and character, is achieved. Finding a solution to improve the quality of SMP in Sawahlunto City based on ANBK using ANN with Backpropagation Algorithm. The data used in this study was sourced from the Education Office of Sawahlunto City where as many as 11 schools participated in ANBK at the junior high school level. Based on the 2021 ANBK simulation data, the results obtained are above the minimum competency of 11 schools. Furthermore, the data is processed using Matlab software. The processing implementation involves four input variables (reading literacy, numeracy, character survey and learning environment survey). Of the 33 data tested using variations of test data and training data, which are then processed using variations in the learning rate and number of epoch parameters. From the test results obtained the level of accuracy of pattern recognition on the backpropagation method with a learning rate variation of 0.2 and the number of epochs 1000. The results of testing this method are as many as 11 junior high schools that have passed. So that the level of accuracy is 99,9987%. The prediction results of ANBK SMP in Sawahlunto City can already describe the quality of education in SMP in Sawahlunto City. With an achievement level above the district/city average of 36.36%, it can become accurate information to improve the quality of teaching and learning and improve student achievement.&#x0D; &#x0D; Keywords: Artificial Neural Networks; Backpropagation; ANBK; Computer-Based National Assessment; Student;&#x0D; Education.