Reviewerly/exHarmony huggingface dataset:
Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm (Extended Abstract)

Most studies on pattern mining have considered only one pattern, such as frequent pattern or high-utility pattern, which is difficult to meet the increasingly diverse needs of users. In this paper, a novel multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed with two kinds of population initialization strategies and an auxiliary tool. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. The proposed algorithm outperforms the state-of-the-art in terms of efficiency, quality, and convergence speed.
RBMS Controlled Vocabulary for Rare Materials Cataloging (RBMS CVRMC)

"RBMS Controlled Vocabulary for Rare Materials Cataloging (RBMS CVRMC)." Technical Services Quarterly, 40(3), pp. 239–240
ASSESSING THE EFFICACY OF LSTM, TRANSFORMER, AND RNN ARCHITECTURES IN TEXT SUMMARIZATION

The need for efficient and effective techniques for automatic text summarization has become increasingly critical with the exponential growth of textual data in different domains. Summarizing long texts into short summaries facilitates a quick understanding of the key information contained in the documents. In this paper, we evaluate various architectures for automatic text summarization using the TEDx dataset, a valuable resource consisting of a large collection of TED talks with rich and informative speech transcripts. Our research focuses on evaluating the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) and Transformer architectures for automatic text summarization. We measure the accuracy of each model by comparing the generated summaries with human-written summaries. The findings show that the Transformer model achieves the highest accuracy, followed closely by the GRU model. However, LSTM, RNN exhibit relatively lower accuracies. We also investigate the trade-off between accuracy and conciseness in summarization. Our study reveals that the Transformer model succeeds in producing accurate and concise summaries, albeit at a higher computational cost. On the other hand, the GRU model strikes a desirable balance between accuracy and conciseness, making it a suitable choice. Overall, this research provides valuable insights into the effectiveness of different architectures for automatic text summarization and highlights the superiority of the Transformer and GRU models in this area.
Flash: A Framework for Programming Distributed Graph Processing Algorithms

As a result of decades of studies, a broad spectrum of graph algorithms have been developed for graph analytics, including clustering, centrality, traversal, matching, mining, etc. However, the majority of recent graph processing frameworks only focus on a handful of fix-point graph algorithms such as breadth-first search, PageRank, shortest path, etc. It leaves the distributed computation of a large variety of graph algorithms suffering from low efficiency, limited expressiveness, or high implementation complexity with existing frameworks.In this paper, we propose Flash, a framework for programming distributed graph processing algorithms, which achieves good expressiveness, productivity and efficiency at the same time. Thanks to its high-level interface, Flash allows users to implement complex distributed graph algorithms with high performance with only a few lines of code. We have implemented 72 graph algorithms for 49 different problems in Flash. In further evaluations, we found that Flash beats other state-of-the-art graph processing frameworks with the speedups of up to 2 orders of magnitudes while takes up to 92% less lines of code.
Introduction

In January 2013, Aaron Swartz, a brilliant computer programmer and activist, died by suicide. At the time, Swartz’s prosecution for engaging in computer fraud – Swartz had reportedly broken into JSTOR, the digital academic library, and had made its content available to the public – was nearing trial in a Boston federal court.2 In the wake of Swartz’s death, critics vehemently blamed the prosecutors spearheading his case for charging him in a way that threatened to produce a potential jail sentence of thirty years’ imprisonment. The United States Attorney contended that Swartz in fact realistically faced no such sentence; indeed, the jail term prosecutors sought in exchange for Swartz’s guilty plea was a mere six months.3 Amidst this back and forth, Swartz’s case eventually receded into the background.
PENDAMPINGAN DAN PELATIHAN PENYUSUNAN LAPORAN KEUANGAN PADA SD MUHAMMADIYAH 8 DAN SMP MUHAMMADIYAH 2 KOTA MALANG

Until now, many schools do not understand how to prepare good and quality financial reports so that they can convey information properly to stakeholders. Lack of understanding due to the competency background possessed can also cause the resulting financial reports to also vary. This can be seen in SD Muhammadiyah 8 and SMP Muhammadiyah Malang City which have different financial reporting systems. On the other hand, as a subsidiary entity under the auspices of PDM Malang City, SD Muhammadiyah 8 and SMP Muhammadiyah 2 Malang City must report their financial reports to PDM so that consolidation can be carried out. This diversity of reporting systems also eventually led to problems for PDM Malang City. Based on this, to improve understanding and ability to produce uniform financial reports, it is necessary to carry out training and assistance on an excel-based financial reporting system as an approach method in solving existing problems.&#x0D;
The artist’s biography in the postmodernism epoch

The objective of the article is to analyze the genesis and age transformation of the artist’s biography; to distinguish the role of a creative personality’s chronicle in the late 21st century; to justify its significant role implementing the anthropological concepts of the period. Research methodology: systemic – by comprehensive solution of the research issues with the involvement of science and methodological elaborations of humanitarian study; culture studies – by identifying the mutual relations between public and spiritual processes; historical – by the grasp of the specifics of the chronicle’s establishment and development; comparative – by comparing the biographies of various centuries; analytical – by considering sources in accordance with the publication issue; theoretical generalization – by summarizing the research results. The article is dedicated to considering the artist’s biography of the Postmodernism epoch. The Postmodernism synthesized elaborations of the preceding eras and on that basis altered the established views on the chronicle’s essence. The genre obtains new functions, i.e. communicative, civilizational, synergetic, etc.; it reveals the era’s controversies having occurred due to the lack of moral ideals, ethics and aesthetics canons and personified leaders. The key task of the biography is realizing the macrocosm structure through recognizing the microcosm of a non-ordinary profile.
Dynamic Topology Identification of Wireless Communication Networks Based on Hawkes Process

ABSTRACT: In the non-cooperative communication network environment, dynamic topology identification of the wireless communication network is a crucial task, because the non-cooperative and dynamic nature of the network poses challenges to topology identification. Information interaction modeling based on the Hawkes process is an emerging direction for non-cooperative network topology identification. The majority of prior topology identification methods based on the Hawkes process only considered static time-invariant topology identification problems, without considering the dynamic nature of the network topology. To tackle this issue, we propose a dynamic topology identification algorithm based on the dynamic window mechanism that is effective in inferencing dynamic network topology. The main contribution of this paper is to model communication events in dynamic networks as multidimensional Hawkes process models. On this basis, combined with the dynamic window mechanism, the Expectation-Maximum method iteratively optimizes the proxy function to identify the topology of the dynamic network. Finally, the identification accuracy performance of link (98.75% accuracy) and non-link (80.11% accuracy) is verified by experiments.
Multi-Scale Contrastive Learning Based Heterogeneous Graph Embedding

Recently, heterogeneous graph contrastive learning has been widely explored as a self-supervised solution for heterogeneous information network embedding. However, based on contrastive learning, most existing graph expansion proposals fail to extract heterogeneous information adequately, and the simple contrastive objective neglects the modelling of the consistency factor among views. In addition, the single criterion of dividing samples makes the problem of sampling bias in contrastive learning obvious. In response to the issues to be solved, we present a multi-scale contrastive learning method (MCLHE) applied to heterogeneous graph embedding. Concretely, we initially set two scales of contrast objectives for cross-schema and cross-view, capturing the consistency factor between complementary information sources. Secondly, we design node-level contrast and graph-level contrast within the contrast objectives, which are combined to convey different granularity of information. Finally, we take into account both structural and feature embedding similarity measures in the sample partitioning process to enhance sampling hardness. Extensive experiments on several public datasets indicate that MCLHE obtains better performance than existing methods.
Attentive Implicit Relation Embedding for Event Recommendation in Event-based Social Network

The event-based social network (EBSN) is a new type of social network that combines online and offline networks, and its primary goal is to recommend appropriate events to users. Most studies do not model event recommendations on the EBSN platform as graph representation learning, nor do they consider the implicit relationship between events, resulting in recommendations that are not accepted by users. Thus, we study graph representation learning, which integrates implicit relationships between social networks and events. First, we propose an algorithm that integrates implicit relationships between social networks and events based on a multiple attention model. Then, the user modeling and event modeling models are fused using a multiattention joint learning mechanism to capture the different impacts of social and implicit relationships on user preferences, improving the recommendation quality of the recommendation system. Finally, the effectiveness of the proposed algorithm is verified in real datasets.
${\sf MARS}$: Enabling Verifiable Range-Aggregate Queries in Multi-Source Environments

The huge values created by Big Data and the recent advances in cloud computing have been driving data from different sources into cloud repositories for comprehensive query services. However, cloud-based data fusion makes it challenging to verify if an untrusted server faithfully integrates data and executes queries or not. This is even harder for range-aggregate queries that apply aggregate operations on data within given ranges. In this paper, we propose a query authentication scheme, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf MARS}$</tex-math></inline-formula> , enabling a user to efficiently authenticate range-aggregate queries on multi-source data. Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf MARS}$</tex-math></inline-formula> creates a VG-tree by subtly integrating Expressive Set Accumulator into a multi-dimensional G-tree while signing the root digest with a multi-source aggregate signature scheme. Compared with previous solutions, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf MARS}$</tex-math></inline-formula> has the following merits: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Practicality.</i> Instead of treating range and aggregate queries separately, the user can directly verify the statistical result of selected data. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scalability.</i> Instead of authenticating the individual result from each source, the user can perform an aggregative validation on the integrated result from multiple sources. The experimental results demonstrate the effectiveness of MARS. For large-scale data fusion, the user-side verification time increases by only 103 ms as the amount of data sources increases by five times.
DSWHAR: A Dynamic Sliding Window Based Human Activity Recognition Method

Sensory data has been widely used for human activity recognition (HAR), where sliding window (SW) is one of the typical methods to segment continuous signals. Most existing HAR methods select fixed-length sliding windows for different activities. However, with complex and diverse human activities, a single fixed sliding window is not suitable for acquiring features of different activities. Some studies have dynamically adjusted the window size based on the activity of feature difference segmentation, but the continuous occurrence of the activity can lead to an oversized window for segmentation and easily introduce additional noise leading to degradation of classification performance. To tackle the above problem, we propose a novel dynamic sliding window based human activity recognition method, called DSWHAR. In the training phase, DSWHAR firstly explores the relationship between the optimal sliding window length and the signal spectrum of each target activity, then a candidate window set is built for the target activity and multiple classifiers are trained using different windows. During the recognition phase, the latest prediction probability is used to dynamically select a suitable window for the next prediction, and the corresponding classifier is used for the final recognition. We compare DSWHAR with the method with fixed sliding windows and dynamic sliding window algorithm on the MHEALTH dataset, respectively, and the experimental results show that DSWHAR not only achieves better overall HAR accuracy but also has unbiased performance on different activities.
A Scenario Based Approach for Context Query Generation

IoT hardware and application silos have achieved a significant progress in recent years. Yet, they have been unable to realize the full IoT potential, due to the lack of middleware platforms that provide horizontal IoT context sharing and integration. Context Management Platforms (CMPs) enable interoperability between IoT silos, and they provide Context-as- a-Service. CMPs enable real-time context-awareness but often lack appropriate mechanisms to support the evaluation of their performance. This paper proposes a novel scenario-based approach for generating context queries that can be used to assess the performance of a CMP, specifically the caching performance. The proposed approach allows a user to generate realistic context queries based on simulated scenarios. The proposed model has the ability to capture the real-world scenarios and translate them into a sequence of scenes. An ontology-based approach is used for scene modelling. Once the scenes are modelled, the situations are inferred through contextual reasoning. The proposed model reveals that inclusion of context information in the scene makes it feasible to handle uncertainty of situations as expected in the real-world. Based on the situations, context queries are generated using different query templates. To support a fair evaluation of the caching mechanism of a CMP, queries with different complexities are generated from the proposed model.
How Does Bot Affect Developer’s Sentiment: An Empirical Study on GitHub Issues and PRs

Software bots act as assistants to help developers in GitHub issues and pull requests (PRs) solution. In this paper, we explore whether software bots affect developer’s sentiment through an empirical study. We find that developers express more neutral sentiment in bot-involved projects and have more positive sentiment in bot-involved issues and PRs. Further, we find developers are less positive in issues and PRs created by bots. To Figure out how developer’s sentiment looks like when mention bots, we use a combination of quantitative and qualitative analyses to explore the different sentiment of developers toward bots and explain the reasons.
Automatic Detection of Stop Words for Texts in Uzbek Language

Stop words are very important for information retrieval and text analysis investigation. This study aimed to automatically analyzed and detect stop words in texts in Uzbek language. Because of limited availability of methods for automatic search of stop words of texts in Uzbek we analyzed a newly prepared corpus. Uzbek language belongs to the family of agglutinative languages. As with all agglutinative languages, we can explain that the detection of stop words in Uzbek texts is a more complex process than in inflected languages: In inflected languages, words such as auxiliary words, articles, prepositions can be included in the stop words group. In agglutinative languages, the meanings of such words are hidden in the text. Therefore, it is not appropriate to apply all known methods of stop words detection in inflected languages directly to agglutinative languages. In this work, the “School corpus” which contains 731156 Uzbek words has been investigated. The bigram method of analysis was applied to the corpus. We proposed the collocation method of detecting stop words of the corpus. We proposed the method of automatically detecting stop words of texts in Uzbek. It is shown that the collocation method is 6 times better than the bigram method.
Contrastive Analysis of Noun Phrase in English and Indonesian on Report Text “Camel” from Developing English Competencies Textbook

This research aims to discover the similarity and differences of Noun Phrase (NP) used in English and Indonesian sentences. This scientific writing used an instrument of report text as the reference to collect data. The report text was taken from a textbook of Developing English Competencies for senior high school. This research demonstrated that when English Noun Phrase was translated to Indonesian, there were changes in syntatic pattern, syntatic category, and meaning. Based on the research results, researchers found several similarities between Indonesian and English Noun Phrases. Similar parts, including head, determiner,and modifier, formed the Noun Phrase. However, there were also differences between them. English Noun Phrase was frequently expanded to the side of the head; meanwhile, in Indonesian, it was expanded to the back of the head. English Noun Phrase consists of a central-determiner (the, a, an); meanwhile, Indonesian Noun Phrase lacks it.
시민사회, 정책확산, 그리고 종교적 반발: 인권조례 도입 과정에 대한 사건사 분석, 2010-2019

Recently, the introduction of human rights ordinances (HROs) has been spreading in South Korea's local governments. However, there is a conspicuously harsh religious backlash from the Protestant communities against the adoption of the ordinance. Accordingly, local governments have been dropping on the proposal and hesitate to adopt HROs. Why did some local governments introduce HROs, while others did not? To answer this question, we analyze what factors affect the passage of HROs by lower-level governments from 2010 to 2019. Based on our discrete event history analysis, results show that civil society factors positively influenced the adoption of HROs, while religious factors negatively affected the adoption of HROs. In particular, the higher the number of Protestant organizations among the lower-level governments that proposed the ordinance, the less likely it was passed. In addition, through the minutes of meetings of local governments, we found that Protestant groups were concerned about the issue of sexual minorities and the spread of anti-discrimination laws, so they want to thwart the adoption of human rights ordinances. The results of our study are meaningful because we found that civil society and religious factors have an important influence in the process of adopting human rights ordinances by local governments.
Optimal quantum encoding of an oscillator into two oscillators

We solve the problem of finding the optimal two-mode oscillator-to-oscillator code for continuous-variable error correction—demonstrating that the two-mode-squeezing code with a hexagonal Gottesman-Kitaev-Preskill state is best.
Integrated Reconfigurable Photonic Matrix Processor from Lithium-Niobate-on-Insulator

We design and demonstrate photonic in-memory computing utilizing broad bandwidth electro-optical modulators from Lithium-Niobate-on-Insulator. A tensor-core configuration gives rise to matrix-vector-multiplications, hence image edge detection.
Cavity-Enhanced Single-Photon Emission from Artificial Atoms in Silicon

We show enhanced single-photon emission from artificial atoms in silicon by coupling them to cavities with high quality factors and small mode volumes, thus enabling enhanced light-matter interactions which are crucial for quantum technologies.
Pengembangan Bahan Ajar Multimoda Transportasi Prodi Diploma Iv Transportasi Laut

The development of teaching materials in this study refers to the ADDIE development model. explained the development of the ADDIE model includes five steps, namely: Analysis, Design, Development, Implementation, and Evaluation. The type of data used is quantitative. Data collection is done by means of a questionnaire to determine the level of eligibility of teaching materials. The results of the study showed the feasibility of the Multimodal Transportation Teaching Material product developed which was declared fit for use according to media and material experts with a percentage of 86.59% and 74.39%, According to the cadets' responses, the user response was declared feasible with a percentage of 76.12% so that the product that had been developed was suitable for use as a learning resource in the learning process of the Multimodal Transportation course.