Reviewerly/exHarmony huggingface dataset:
Copyright Protection and Data Reliability of AI-Written Literary Creations in Smart City

The purpose is to solve copyright disputes over artificial intelligence (AI)-written literary creations and protect copyright by legislation through data reliability research. Accordingly, this work makes a detailed study of the criteria for the copyright protection of AI-written literary creation and the creation process. It constructs the model of swarm intelligence (SI) perception. Then, the Preservation Trustworthiness Incentives Sense (PTISense) scheme is designed based on the encryption algorithm for the SI perception model. The performance of the proposed PTISense scheme is verified and analyzed through experiments. Mainly, it analyzes the impact of PTISense on the accuracy of the reputation model, its robustness against malicious users, and the actual feasibility. The results show that when users complete 50 tasks, the false-positive rate is only 0.1, and the corresponding false-negative rate approximates 0. After each task, the user reputation will be updated, and the data will be evaluated for trust. The trust model of the proposed PTISense scheme based on encryption technology is more accurate. When η (the number of malicious users) is small, the more tasks are performed, the faster the reputation value decreases and tends to zero. The proposed PTISense scheme-based reputation evaluation model can better protect the data submitted by good users. It is robust against malicious users and protects the data and privacy of good users. Further, entities’ computing overhead in different SI perception stages is calculated. It is found that the proposed PTISense scheme is feasible for user data privacy protection. Compared with other schemes, it can achieve a safe and reliable SI perception process with a lower computing overhead. It can better ensure the authenticity and reliability of data.
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation

With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emergence of new nodes and edges. Novel classes appear incrementally along with few labeling due to its newly emergence or lack of exploration. In this paper, we focus on this challenging but practical graph few-shot class-incremental learning (GFSCIL) problem and propose a novel method called Geometer. Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype. Prototype is a vector representing a class in the metric space. With the pop-up of novel classes, Geometer learns and adjusts the attention-based prototypes by observing the geometric proximity, uniformity and separability. Teacher-student knowledge distillation and biased sampling are further introduced to mitigate catastrophic forgetting and unbalanced labeling problem respectively. Experimental results on four public datasets demonstrate that Geometer achieves a substantial improvement of 9.46% to 27.60% over state-of-the-art methods.
PARSRec

The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.
Analysis of Affective Factors and Optimization Strategies of Emotion in Online Teaching Based on Improved SVM Model

Online teaching has the advantages of not being limited by location and space but it also has some shortcomings. The lack of face-to-face real-time interaction between teachers and students will affect some students’ learning mood. The improved support vector machine (SVM) model is a simple model based on linear algebra, which can convert text data into structured data that can be processed by a computer and then calculate the similarity between two documents into the similarity between two vectors. The facial expression features of learners in the situation collected and extracted by the students of this project group are analyzed and modeled, and the time consumption, occupied space, and classification effect of the feature vectors produced by the improved model are integrated. The original feature dimension can be optimized from 100 dimensions to 60 dimensions, which not only saves the time of training feature vectors but also reduces the size of the final feature vectors. Besides, on the basis of 60-dimensional preliminary features extracted by SVM model, four classification models can also achieve the best results. Therefore, in the optimization part of feature extraction, the dimension of initial features extracted by SVM model is set to 60 dimensions. We can gradually use the improved SVM model to analyze the emotional influencing factors and optimization strategies in online teaching, so as to keep abreast of students’ lectures and let more students participate in online teaching as much as possible.
Cumulonimbus: An Incentive Mechanism for Crypto Capital Commitment in Payment Channel Networks

Payment channel networks (PCNs) are proposed to improve the cryptocurrency scalability by settling off-chain transactions. However, a significant barrier is that a PCN user must solicit sufficient capital owned by the counterparty on its channel (i.e., inbound liquidity) to receive payments. To alleviate this inbound liquidity problem, Channel Liquidity Marketplaces (CLMs), e.g., Bitcoin's Lightning Pool, have been introduced, such that users can buy and sell inbound liquidity by trading crypto capital commitment in PCNs. Existing CLMs lack good incentive mechanisms that can attract more user participation. To fulfill this void, we design Cumulonimbus, an incentive mechanism for trading crypto capital commitment, which satisfies truthfulness, individual rationality, budget balance, and computational efficiency. Particularly, Cumulonimbus considers two unique features of crypto capital commitment, referred to as demand indivisibility and supply divisibility. Extensive simulations demonstrate that Cumulonimbus achieves higher satisfaction ratio, liquidity utilization, and social welfare compared with a state-of-the-art CLM mechanism Lightning Pool [18].
A Rule-Based Method to Locate the Bounds of Neural Networks

An advanced method of training artificial neural networks is presented here which aims to identify the optimal interval for the initialization and training of artificial neural networks. The location of the optimal interval is performed using rules evolving from a genetic algorithm. The method has two phases: in the first phase, an attempt is made to locate the optimal interval, and in the second phase, the artificial neural network is initialized and trained in this interval using a method of global optimization, such as a genetic algorithm. The method has been tested on a range of categorization and function learning data and the experimental results are extremely encouraging.
Matching Cybersecurity Ontologies on Internet of Everything through Coevolutionary Multiobjective Evolutionary Algorithm

Since Internet of Everything (IoE) makes all the connections that come online more relevant and valuable, they are subject to numerous security and privacy concerns. Cybersecurity ontology is a shared knowledge model for tackling the security information heterogeneity issue on IoE, which has been widely used in the IoE domain. However, the existing CSOs are developed and maintained independently, yielding the CSO heterogeneity problem. To address this issue, we need to use the similarity measure (SM) to calculate two entities’ similarity value in two CSOs and, on this basis, determine the entity correspondences, i.e., CSO alignment. Usually, it is necessary to integrate various SMs to enhance the result’s correctness, but how to combine and tune these SMs to improve the alignment’s quality is still a challenge. To face this challenge, this work first models CSO matching problem as a Constrained Multiobjective Optimization Problem (CMOOP) and then proposes a Coevolutionary Multiobjective Evolutionary Algorithm (CE-MOEA) to effectively address it. In particular, CE-MOEA uses the multiobjective evolutionary paradigm to avoid the solutions’ bias improvement and introduces the coevolutionary mechanism to trade off Pareto Front’s (PF’s) diversity and convergence. The experiment uses Ontology Alignment Evaluation Initiative’s (OAEI’s) bibliographic track and conference track and five real CSO matching tasks to test CE-MOEA’s performance. Comparisons between OAEI’s participants and EA- and MOEA-based matching techniques show that CE-MOEA is able to effectively address various heterogeneous ontology matching problems and determine high-quality CSO alignments.
PENGEMBANGAN MEDIA SCRAPBOOK DIGITAL MODEL PROBLEM-BASED LEARNING PADA MUATAN IPS

Abstrak&#x0D; Tujuan penelitian ini adalah mengembangkan, menguji kelayakan, dan menguji keefektifan pada media pembelajaran Scrapbook Digital model Problem-Based Learning (PBL) muatan IPS materi manfaat sumber daya alam dalam bidang ekonomi kelas IVB SD Sampangan 01. Penelitian ini menggunakan desain penelitian pengembangan dengan one group pretest posttest. Subjek penelitian ini adalah guru dan 36 siswa kelas IVB SD Negeri Sampangan 01 Semarang Teknik pengumpulan data menggunakan wawancara, observasi, angket, tes, dan dokumentasi. Analisis yang digunakan adalah uji normalitas, uji t-test dan uji peningkatan rata-rata (N-Gain). Hasil penelitian menunjukkan bahwa: (1) Dalam mengembangkan media Scrapbook Digital model PBL menggunakan software Corel Draw x7 untuk desain gambar dan Macromedia Flash 8 untuk menggabungkan semua komponen; (2) Persentase penilaian media Scrapbook Digital model PBL oleh ahli media sebesar 74% (Layak) dan ahli materi sebesar 88% (Sangat Layak); (3) Pada uji normalitas data pre-test post-test berdistribusi normal, pada uji t-test diperoleh thitung = 6,292 &gt; ttabel = 2,030 maka Ha diterima, hasil uji N-gain sebesar 0,37 dengan kriteria sedang. Simpulan penelitian ini adalah media pembelajaran Scrapbook digital model PBL layak dan efektif digunakan dalam pembelajaran mupel IPS materi manfaat sumber daya alam dalam bidang ekonomi.&#x0D; The purpose of the research was to develop, test the feasibility, and test the effectiveness of the Digital Scrapbook media with the Problem-Based Learning (PBL) model in social studies with content the benefits of natural resources in the economic field for grade 4th B Sampangan 01 elementary school. This study used a development research design with one group pretest posttest, the subjects of this study were teacher and 36 students grade 4th B Sampangan 01 elementary school. The data collection technique used interviews, observations, questionnaires, tests, and documentation. The analysis used normality test, t-test and average increase test (N-Gain). The results showed that : (1) in development Digital Scrapbook Media with PBL model using Corel Draw x7 software for image design and Macromedia Flash 8 to combine all components; (2) Percentage assessment of Digital Scrapbook Media with PBL model by media experts is 74% (eligible) and material experts is 88% (Very Eligible); (3) In the normality test, the pre-test post-test data were normally distributed, the t-test test obtained tcount = 6,292 &gt; ttable = 2,030 then Ha was accepted, N-gain test result was 0.37 with moderate criteria. In conclusion, the digital scrapbook media with PBL model is feasible and effective to be used in social studies learning about the benefit of natural resources in the economic field.
Discovering the Optimal Number of Crime Cluster Using Elbow, Silhouette, Gap Statistics, and NbClust Methods

In recent years, crime has been critical to be analyzed and tracked to identify the trends and associations with crime patterns and activities. Generally, the analysis is conducted to discover the area or location where the crime is high or low by using different clustering methods, including k-means clustering. Even though the k-means algorithm is commonly used in clustering techniques because of its simplicity, convergence speed, and high efficiency, finding the optimal number of clusters is difficult. Determining the correct clusters for crime analysis is critical to enhancing current crime resolution rates, avoiding future incidents, spending less time for new officers, and increasing activity quality. To address the problem of estimating the number of clusters in the crime domain without the interference of humans, the research carried out Elbow, Silhouette, Gap Statistics, and NbClust methods on datasets of Major Crime Indicators (MCI) in 2014−2019. Several stages were performed to process the crime datasets: data understanding, data preparation, cluster modelling, and cluster validation. The first two phases were performed in the R Studio environment and the last two stages in Azure Studio. From the experimental result, Elbow, Silhouette, and NbClust methods suggest a similar number of optimum clusters that is two. After validating the result using the average Silhouette method, the research considers two clusters as the best clusters for the dataset. The visualization result of Silhouette method displays the value of 0,73. Then, the observation of the data is well-grouped. It is placed in the correct group.
Prediction of Undergraduate Student’s Study Completion Status Using MissForest Imputation in Random Forest and XGBoost Models

The number of higher education graduates in Indonesia is calculated based on their completion status. However, many undergraduate students have reached the maximum length of study, but their completion status is unknown. This condition becomes a problem in calculating the actual number of graduates as it is used as an indicator of higher education evaluation and other policy references. Therefore, the unknown completion status of the students who have reached the maximum length of study must be predicted. The research compared the performance of Random Forest and Extreme Gradient Boosting (XGBoost) classification models in predicting the unknown completion status. The research used a dataset containing 13.377 undergraduate students’ profiles from the Higher Education Database (PDDikti), Ministry of Education, Culture, Research, and Technology. The dataset was incomplete, and the proportion of missing data was 20,9% of the total data. Because missing data might lead to prediction bias, the research also used MissForest imputation to overcome the missing data in the classification modelling and compared it to Mean/Mode and Median/Mode imputation. The results show that MissForest outperforms the other two imputations in both classifiers but requires the longest computation time. Furthermore, the XGBoost model with MissForest is significantly superior to the Random Forest model with MissForest. Hence, the best model chosen to predict the completion status is XGBoost with MissForest imputation.
A Reconsideration of the Foundations of Identity Management

There is widespread recognition that, during the process of digitalisation, much greater care is necessary in relation to the needs of individuals and society. One key area in which tensions exist is identity management. People think that their identities are intrinsic to themselves. Yet organisations represent themselves as 'provisioning' people with their 'identities'. In addition, the model of identity that organisations typically use evidences some important deficiencies. A fresh approach is needed to the model that underpins organisations' management of their relationships with people. This needs to be based on a deeper appreciation by designers of the nature of the phenomena that they seek to document and to exercise control over. A model of those phenomena is needed that is pragmatic, in the sense of fulfilling the needs of information systems (IS) practitioners and organisations, but also of the people whose data the organisation handles. It also needs to reflect metatheoretic insights. This paper presents such a model. It commences by drawing on ontology, epistemology and axiology in order to establish an outline metatheoretic model. The model is articulated, at the conceptual level and at the data modelling level. Initially, a relatively simple model is established, sufficient for inanimate objects and artefacts. The more complex requirements of humans are then addressed. It is contended that the resulting model provides a robust framework for identification and authentication in IS.
Encoding Interruptions in Parliamentary Data: From Applause to Interjections and Laughter

Parliamentary data, especially parliamentary discourse, is of interest to researchers from various fields in the humanities and social sciences. The growing number of machine-readable and annotated parliamentary text corpora opens up this field for computer-based quantitative analysis. In this paper I aim to give an overview of how parliamentary interruptions are recorded in official parliamentary records and how they are modeled and encoded in currently available machine-readable parliamentary corpora. Furthermore, I will discuss whether these encodings are suitable for computer-based quantitative analysis of parliamentary interruptions. I will suggest detailed encodings of parliamentary interruptions in TEI as an extension to the Parla-CLARIN recommendations, to enable the extraction of parliamentary interruptions and to facilitate computerized quantitative analysis based on these encodings. As an example, I will use the encoding of interruptions in the Austrian Parliamentary Records.
Superior Class to Improve Student Achievement Using the K-Means Algorithm

The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values ​​for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.
Neural Architecture Search: A Visual Analysis

Neural architecture search (NAS) refers to the use of search heuristics to optimise the topology of deep neural networks. NAS algorithms have produced topologies that outperform human-designed ones. However, contrasting alternative NAS methods is difficult. To address this, several tabular NAS benchmarks have been proposed that exhaustively evaluate all architectures in a given search space. We conduct a thorough fitness landscape analysis of a popular tabular, cell-based NAS benchmark. Our results indicate that NAS landscapes are multi-modal, but have a relatively low number of local optima, from which it is not hard to escape. We confirm that reducing the noise in estimating performance reduces the number of local optima. We hypothesise that local-search based NAS methods are likely to be competitive, which we confirm by implementing a landscape-aware iterated local search algorithm that can outperform more elaborate evolutionary and reinforcement learning NAS methods.
Reforming the Teaching of Engineering Cost Budgeting Majors by Integrating VR and BIM Technology under the Internet of Things

With the continuous development of digital technology and the Internet of Things (IoT), the teaching methods for architecture major in higher vocational colleges have also undergone major changes. New technologies and instruction methods in Engineering Cost Budgeting teaching can stimulate students’ learning interest and improve education quality and students’ comprehensive learning ability. In order to improve the teaching level of engineering cost budgeting major and stimulate students’ interest in learning, this work first introduces backpropagation neural network (BPNN) into engineering cost estimation (ECE). Then, the BPNN-based ECE model is trained by the sample data to estimate the project’s total quotation and comprehensive unit price. The error between the real and predicted values is analyzed. Second, the building information modeling (BIM) technology and virtual reality (VR) technologies are integrated into teaching engineering cost budgeting. The investigation, research, and analysis are conducted before and after applying BIM and VR technology in practical teaching. The results show that the proposed BPNN-based ECE model-estimated total quotation and comprehensive unit price fit well the sample values. The BPNN-based ECE model can be applied to teaching engineering cost budgeting. It can improve the calculation accuracy, and the relative error can be controlled within a certain range and has a certain potential to replace manual budgeting. It can provide some reference for the research of engineering cost technology. Classroom teaching under the integration of BIM and VR technologies can improve the students’ homework quality, academic performance, and teaching quality to a certain extent. Integrating BIM and VR technology in classroom teaching can enhance students’ communication, cooperation ability, oral defense scores, comprehensive scores, and professional skills.