Reviewerly/exHarmony huggingface dataset:
STREAMER 3.0: Towards Online Monitoring and Distributed Learning

Applications that generate continuous data have proliferated in recent years, and thus the challenge of processing those data streams has emerged. This requires Data Stream Processing frameworks with monitoring capabilities able to detect and react to any non-desired situation. Many streaming use cases deal with distributed sources of data which, for privacy and communication saving purposes, need to be tackled in a distributed manner. Based on the mentioned challenges, this paper presents STREAMER 3.0, an improvement on the former data stream framework with two new modules: (i) a monitoring manager with detection algorithms, alert raising and automatic model updater; and (ii) a distributed learning module relying on federated learning. We showcase these new functionalities with an example of remaining useful life estimation of turbofan engines using an LSTM.
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning

The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of items purchased together. As a less studied interaction mode in the community, the question of how should shopping basket complement personalized recommendation systems remains under-explored. While previous attempts focused on jointly modeling user purchases and baskets, the distinct semantic nature of these elements can introduce noise when directly integrated. This noise negatively impacts the model's performance, further exacerbated by significant noise (e.g., a user is misled to click an item or recognizes it as uninteresting after consuming it) within both user and basket behaviors. In order to cope with the above difficulties, we propose a novel Basket recommendation framework via Noise-tolerated Contrastive Learning, named BNCL, to handle the noise existing in the cross-behavior integration and within-behavior modeling. First, we represent the basket-item interactions as the hypergraph to model the complex basket behavior, where all items appearing in the same basket are treated as a single hyperedge. Second, cross-behavior contrastive learning is designed to suppress the noise during the fusion of diverse behaviors. Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w.r.t augmentations through within-behavior contrastive learning. A novel consistency-aware augmentation approach is further designed to better identify the noisy interactions with the consideration of the above two types of interactions. Our framework BNCL offers a generic training paradigm that is applicable to different backbones. Extensive experiments on three shopping transaction datasets verify the effectiveness of our proposed method.
FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences with 78 instantiated grammar points and 3 types of edits. We evaluate various cutting-edge CGEC methods on the proposed FlaCGEC dataset and their unremarkable results indicate that this dataset is challenging in covering a large range of grammatical errors. In addition, we also treat FlaCGEC as a diagnostic dataset for testing generalization skills and conduct a thorough evaluation of existing CGEC models.
Cognitive-inspired Graph Redundancy Networks for Multi-source Information Fusion

The recent developments in technologies bring not only increasing amount of information but also multiple information sources for Graph Representation Learning. With the success of Graph Neural Networks (GNN), there have been increasing attempts to learn representation of multi-source information leveraging its graph structures. However, existing graph methods basically combine multi-source information with different contribution scores and over-simplify the graph structures based on prior knowledge, which fail to unify complex and conflicting multi-source information. Multisensory Processing theory in cognitive neuroscience reveals human mechanism of learning multi-source information by identifying the redundancy and complementarity. Inspired by that, we propose Graph Redundancy Network (GRN) that: 1). learns a suitable representation space that maximizes multi-source interactions; 2). encodes the redundant and complementary information according to Graph Intersection and Difference of their graph structures; 3). further reinforces and explores the redundant and complementary information through low-pass and high-pass graph filters. The empirical study shows that GRN outperforms existing methods on various tasks.
Geometry Interaction Augmented Graph Collaborative Filtering

Graph collaborative filtering, which could capture the abundant collaborative signal from the high-order connectivity of the tree-likeness user-item interaction graph, has received considerable research attention recently. Most graph collaborative filtering methods embed graphs in the Euclidean spaces, but that could have high distortion when embedding graphs with tree-likeness structure. Recently, some researchers address this problem by learning the feature representations in the hyperbolic spaces. However, because the user-item interaction graphs also have cyclic structure, the high-order collaborative signal cannot be well captured by hyperbolic spaces. From this point of view, neither Euclidean spaces nor hyperbolic spaces can capture the full information from the complexity of user-item interactions. Therefore, how to construct a suitable embedding space for graph collaboration filtering is an important problem. In this paper, we analyze the properties of hyperbolic geometry in graph collaborative filtering tasks and proposed a novel geometry interaction augmented graph collaborative filtering (GeoGCF) method, which leverages both Euclidean and hyperbolic geometry to model the user-item interactions. Experimental results show the effectiveness of the proposed method.
KGPR: Knowledge Graph Enhanced Passage Ranking

Passage ranking aims to rank a set of passages based on their relevance to a query. Current state-of-the-art models for this task typically employ a cross-encoder structure. However, these models lack access to background knowledge, i.e., information related to the query that can be helpful in retrieving relevant passages. Knowledge Graphs (KGs) provide a structured way of storing information about entities and their relationships, offering valuable background knowledge about entities. While KGs have been used to augment pretrained language models (LMs) to perform several reasoning tasks such as question answering, it remains an open question of how to utilise the information from KGs to enhance the performance of cross-encoders on the passage ranking task. Therefore, we propose KGPR, a KG-enhanced cross-encoder for the Passage Retrieval task. KGPR is built upon LUKE, an entity-aware pretrained LM, with an additional module that fuses information from KGs into LUKE. By leveraging the background knowledge from KGs, KGPR enhances the model's comprehension of queries and passages, resulting in improved ranking performance. Experimental results demonstrate that using KGs can enhance the performance of LUKE in the passage retrieval task, and KGPR can outperform state-of-the-art monoT5 cross-encoder by 3.32% and 10.77% on the MS MARCO development set and TREC DL-HARD query set respectively, using a model with a similar number of parameters.
Stochastic Subgraph Neighborhood Pooling for Subgraph Classification

Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph (e.g., identifying rare diseases given a collection of phenotypes). Graph neural network (GNN) solutions for node, link, and graph tasks fail to perform well on subgraph classification as they do not capture the external topology of the subgraph (i.e., how the subgraph is located within the larger graph). The current state-of-the-art models address this shortcoming through either labeling tricks or multiple message-passing channels, which are computationally expensive and not scalable to large graphs. To address the scalability issue while maintaining generalization, we propose Stochastic Subgraph Neighborhood Pooling (SSNP), which jointly aggregates the subgraph and its neighborhood (i.e., external topology) information while removing the need for any computationally expensive operations (e.g. labeling tricks). Our extensive experiments demonstrate that SSNP outperforms or is comparable to state-of-the-art methods while being up to 13x faster in runtime.
Topological Network Field Preservation for Heterogeneous Graph Embedding

Heterogeneous graph (HG) embedding, aiming to represent the nodes in the graph as a low-dimensional vector form for further reasoning to better implement downstream tasks, has attracted considerable attention in recent years. Most existing HG embedding methods use the meta-paths to preserve the proximity or adapt graph neural networks (GNNs) to facilitate the message-passing process. However, these methods neglect to analyze the shape properties of nodes and the influence of each node from a topological perspective, thus cannot fully explore the information on higher-order connectivity of HG and be effectively support more complex tasks of network analysis. In this paper, a novel HG embedding model (TNFE) is proposed to capture the topological link structure and the higher-order interactive information between nodes simultaneously. Specifically, persistent homology is used to reconstruct the connection between nodes in HG. Then the neighborhoods of the nodes are aggregated based on a graph convolutional network. Moreover, modular topology centrality is defined to sample the topological network field structure of each node. Finally, multi-task learning task is built to preserve the topology connectivity and the topological network field proximity simultaneously. The extensive experiments on three real-world datasets show that our method outperforms the state-of-the-art approaches on node classification and clustering task.
A Study on the Effectiveness Evaluation of Job Training for Marine Police Officers and Improvement Measures

The purpose of this study is to analyze the effectiveness of marine police officers’ job training based on Kirkpatrick's evaluation model of training effectiveness and to contribute to the development of marine police officer training by making policy suggestions based on the analysis results. In order to achieve this research purpose, the four levels of Kirkpatrick's evaluation model of training effectiveness were examined, prior studies were analyzed based on Kirkpatrick's evaluation model, and the effectiveness of job training was empirically tested using the data from 856 police officers.&#x0D; Multiple regression analyses showed that the factors affecting the effectiveness of job training for marine police officers were the training curriculum, training method, learning effect, and learning transfer. Therefore, in order to improve the effectiveness of job training, the department in charge of marine police training needs to develop the training curriculum that meets the internal and external needs of coast guard, implement the training methods suitable for the characteristics of the training subject, and as a result, facilitate learning effect and learning transfer more.
A Study on Teachers' Perception and Implementation of English Class Using EduTech: Focused on the Focus Group Interview.

The purpose of this study is to find out teachers' perceptions and implementation of English class using edutech. An Online survey and a focus group interview were conducted. The results of the study are as follows. First, teachers mentioned that the purpose of using edutech is to improve students' activity and induce interest. Second, regarding the experience of using edutech, they used Classcard and Chatterfix the most, and stated that students' speaking ability and concentration have improved. Third, as a result of examining teachers' perceptions of edutech, the most common response was that more classes using edutech should be increased for future education. Fourth, they responded that the advantage of English class using edutech was to induce interest and achievement. On the other hand, they pointed to environmental restrictions and difficulties of use as disadvantages. Based on these research results, this paper presents several suggestions.
Responsibilities and Implications of External Auditors through Case of Accounting Fraud in the Order－made Production Industry

[Purpose] The order－made production industry, including the construction and shipbuilding industries, recognizes revenue based on the progress rate. Because progress estimates involve complex assumptions and estimates, they have high inherent risk and high audit risk. As a result, accounting fraud often occurs in order－made industries such as construction and shipbuilding. This study analyzes the accounting fraud cases of Company A in the ordermade production industry from 2008 to 2015 to present legal responsibilities for external auditors' poor auditing and implications of the court’s ruling on Company A’s accounting fraud on external audit.&#x0D; [Methodology] We conducted a search of court rulings related to Company A’s accounting fraud lawsuit and literature published by relevant supervisory authorities.&#x0D; [Findings] First, external auditors must strive to strengthen their competence and independence. Second, the scope of responsibility of the audit team must be made clear. Third, if the audited company’s submission of audit－related data is insufficient it is necessary to increase the differential information value of the audit report by actively considering modifying the audit opinion. Fourth, active communication between the auditor and the audited company is necessary, and after communication, audit risks must be managed by documentation.&#x0D; [Implications] By analyzing cases of accounting fraud in the order－made production industry based on court rulings, this study presented how the auditor’s perspective on accounting audits differs from the court’s perspective. In addition, it is different in that it analyzes accounting fraud cases from the auditor’s responsibility perspective and suggests implications.
Police Science Competency-based Education Plan Reflecting Core Competencies for Each Police Job

In response to changes in the internal and external environment of universities along with changes in the police organization in Korea, changes are needed in the education of four-year police departments nationwide. Accordingly, police-related departments that have the educational goal of cultivating excellent public safety experts propose a plan to organize and operate a competency-based curriculum suitable for them. For competency-based education, the core competency of the police was derived by analyzing 「Guidebook on Core Competence by Police Job」 published by the National Police Agency. As a result of the analysis, the core competencies of the police are 7: situation judgment, problem solving, collaboration (teamwork), communication, interpersonal relationships, integrated thinking, understanding and application of laws, and these are reflected in the police science competency-based curriculum. Applying innovative teaching methods rather than setting competencies in individual subjects is more appropriate for competency-based education. Various innovative teaching methods such as flipped learning, discussion class, problem-based learning, and project-based learning should be applied to the police science curriculum. Police science competency-based education using innovative teaching methods requires not only the interest and efforts of the instructors, but also the willingness to participate and a lot of effort from the learners, from course design to operation and evaluation. This will contribute to sharing the results of competency-based education and designing a standardized competency-based curriculum. Since there are limitations in cultivating the core competencies of the police through the major curriculum, the lacking parts must be supplemented through liberal arts and extracurricular courses.
VARIASI FORMULA PUMAKKAL TERHADAP KADAR N, P, K PADA PUPUK CAIR LIMBAH PRODUKSI MIE SEBAGAI PENDUKUNG BELAJAR BIOLOGI BERUPA LEMBAR KEGIATAN PESERTA DIDIK (LKPD)

This study aimed to determine the variation of the pumakkal formula on the levels of N, P, K in the liquid fertilizer of noodle production waste, to determine the variation of the pumakkal formula according to the Indonesian National Standard (SNI) as a material for constructing support for learning biology. This type of research is an experimental study using the pumakkal formula P1 (3 bacteria), P2 (6 bacteria), P3 (9 bacteria), P4 (12 bacteria), and P5 (15 bacteria). The parameter observed was the content of Nitrogen (N) levels. Phosphorus (P), and Potassium (K). Analyzed using qualitative methods by comparing the results with the SNI for liquid fertilizers. Based on the results of the study, there was an effect of variations in the pumakkal formula on the liquid waste of noodle production. testing with SNI for liquid fertilizer showed that treatment 3 (P3) with a percentage yield of 2,092, treatment 4 (P4) with a result of 2,386, and treatment 5 (P5) with a result of 2,445 met SNI, and the best results were in treatment 5 (P5). Based on the validation analysis, learning support has a percentage value of 97.5%, so it is feasible to be used as a support for learning biology in the form of Student Activity Sheets (LKPD).
A Study on the Preparing Activity for investigation and Investigation Process of the Osong Disaster

Based on government agency data and media reports, this study examined the progress of the Osong disaster investigation and examined several characteristic issues that have emerged in the process of police investigation, inspection by the State Affairs Coordination Office, and prosecution investigation.&#x0D; First, Chungbuk Police Agency after the Osong disaster, lost its power when the Office of Public Service Management initiated service inspection. Since then, the investigative agency has been changed from police to prosecution, and the police investigation headquarters has been disbanded. Second, the Office of the Public Service Management requested the Supreme Prosecutors' Office to investigate those involved in the case, including civilians, through rapid inspection activities. The fact that the Office of Public Service Management will investigate civilians and request an investigation is an activity that publicly expressed that it will no longer stay at the public official inspection agency but play its role as an preparatory investigation agency that investigates civilians. Third, based on the request for investigation, the Supreme Prosecutors' Office established an investigation headquarters at the Cheongju District Prosecutors' Office, and the case has been investigated and investigated so far. This investigation and investigation behavior is typical of prosecutors opening investigations through a request for investigation by a state agency, and is based on each item of Article 4 (1) 1 of the Prosecutors' Office Act, and in the case of important crimes prescribed by Presidential Decree (a item of the same issue), it is outside the limits of the delegation legislation and is at the center of controversy over unconstitutionality and illegality.
PENGEMBANGAN E-MODUL BERBASIS INKUIRI TERBIMBING TERINTEGRASI NILAI-NILAI KARAKTER PADA MATERI SISTEM PENCERNAAN MANUSIA KELAS XI

This development research aims to produce teaching material products in the form of guided..inquiry-based e-modules integrated with character values ​​in human digestive system materials that are suitable for use. The development of this e-module uses a 4-D model, covering four stages, namely, Define (analyze needs), Design (design e-module products), Develop (develop designed e-module products), and Disseminate (Product deployment). The instrument used is an assessment questionnaire. There are two types of questionnaires used, namely the first expert validation questionnaire with 2 assessments (material and design). Both student response questionnaires. The e-module is validated by design and material experts, both validators provide suggestions and comments to improve the e-module. Products that have been revised on the advice of design experts get an average percentage score of 95% and material experts get an average..percentage score of 91%. The e-module product was tested in Class XI IPA SMA Muhammadiyah 2 Metro. Getting a score of all aspects getting a percentage of 89%. The results of expert validation and student responses show that the e-module developed is "Very Appropriate". So that it can be used by students and teachers in the online and offline learning process. This development research has limitations, namely in the stage of spreading it on a small scale.
The Identity of a Null Determiner in So-Called Determiner-Less Languages: Evidence from Null Arguments in Korean

There is no shortage of analyses for null arguments in Korean/Japanese: pro, V-stranding VP ellipsis, null bare noun, and argument ellipsis. This paper modifies the third null bare noun analysis to reinforce a null determiner-stranding NP ellipsis analysis for null arguments in Korean/Japanese, which Tomioka (2003) initially proposes (see also Hoji (1998) for the seminal proposal). This paper argues that the null determiner licensing an elision of its preceding complement NP is identified with a non-overt determiner that productively occurs with an overt bare (i.e., modifier-less) noun in Korean/Japanese, but comes with ellipsis-licensing E-features (Merchant 2001). When it is an indefinite one (like the English overt counterpart ‘a/an’), the null article cannot be compatible with the following null NP composed of such an element as a universal quantifier, a numeral, and an indeterminate, accounting for Landau’s (2022) recent generalization on the restricted construal of a null argument (i.e. null NP plus null determiner). When it is a definite one, the null article that comes with a null bare noun projection cannot be, either, giving rise to a non-quantificational/referential E-type reading of the null argument that excludes such an element. Overall, the null determiner and its nature and distribution are shown to resolve ever-puzzling aspects of null arguments in Korean.