Reviewerly/exHarmony huggingface dataset:
ENASA: Towards Edge Neural Architecture Search based on CIM acceleration

This work proposes a ReRAM-based Computing-in-Memory (CIM) architecture for Neural Architecture Search (NAS) acceleration, ENASA, so that the compute-intensive NAS technology can be applied to various edge devices to customize the most suitable individual solution for their cases. In the popular one-shot NAS process, the system must repetitively evaluate the sampled sub-network within a large-scale supernet before converging to the best sub-network architecture. Thereby, how to map these iterative network inference tasks onto the CIM arrays makes a big difference in system performance. To realize efficient in-memory supernet sampling and evaluation, we design a novel mapping method that tactically executes a group of sub-nets in the CIM arrays, not only to boost the sub-net concurrency but also to eliminate the repetitive operations shared by these subnets. Meanwhile, to further enhance the subnet-level operation concurrency and sharing in the CIM arrays, we also tailor a novel CIM-friendly one-shot NAS algorithm that purposely samples those operation-sharing subnets in each iteration while still maintaining the convergence performance of NAS. According to the experimental results, our CIM NAS accelerator achieves an improvement of 196.6× and 1200× in performance speedup and energy saving respectively compared to the CPU+GPU baseline.
Vertical Federated Learning Architecture for Power Company and Financial Company and Electricity Pricing Model Considering User Credit Evaluation

With the development of the electric power system, the construction of electric power credit has achieved positive results, but there is still a certain gap compared with the requirements of the government and enterprises. In this paper, a vertical federated learning framework including user credit evaluation is proposed. By constructing a vertical federated learning credit sharing system between electric power companies and financial companies, the information barriers of both are reduced and the market transaction risks are reduced. Through the construction of refined electricity price pricing model based on user credit evaluation, it is beneficial to reduce the cost and increase the efficiency of users, and encourage users to develop with high credit and high quality.
Possible Impacts of Smart Contracts on Construction Claim Management

The information age and digital transformation challenge the construction industry's traditional business methods and current dynamics. Smart contracts are recently adopted in the construction industry to digitally facilitate the exchange of money, content, property, shares, and digitized asset. In this context, the usage of smart contracts is expected to impact the claims encountered in construction projects and the traditional claim management processes. This study aims to analyze the possible effects of smart contracts on construction claim management. A structured literature analysis was undertaken to determine the benefits that smart contracts may offer in this context. As a result, a research roadmap is developed to discuss the steps needed to integrate smart contracts in the construction claim management field. The study findings show that smart contracts may have considerable benefits in the claim management process including, record integrity, elimination of unnecessary bureaucracy, reliability of documents, reduction of transaction costs, and enhancing transparency.
Pemanfaatan Google Scholar Dalam Pencarian Informasi

&#x0D; In today's technological developments, many academics, including students, are writing digital documents. However, finding the right reference source and determining whether an article contains elements of plagiarism or is still experiencing difficulties. Until now, many academics have found information from unreliable sources, even though if this continues it can have a negative impact on academics. Therefore, technology through Google provides easy access to handle this by making Google Scholar one of the trusted places to find legitimate articles. This research uses descriptive qualitative method. The author hopes that with this writing, students can obtain more comprehensive information and be able to make it easier for examiners to find plagiarism in student final assignments.&#x0D; Keywords: Information Technology, Google scholar, Information search
Korean Twitter Bot Detection via Deep Learning

We introduce a neural network model detecting Korean malicious bot account in Twitter. We extracted features from Korean malicious bot account in Twitter and implemented a model in the context of classification between human and bot account. This can be seen as an improvement of malicious bot detection, in an area of accuracy and multilingual aspect. The mode also suggests an end-to-end solution framework by offering web service.
Design of 2D to 3D Pose Estimation Using NeRF Image View Synthesis

Skeleton construction and pose estimation through two-dimensional (2D) images may have reduced estimation accuracy due to truncation and occlusion that may occur within the image. In this paper, a 3D model is created through NeRF (Neural Radiance Fields) based Instant-NGP (Neural Graphics Primitives), a skeleton is constructed, and the artificial intelligence is trained to perform pose estimation in a 2D image. Create a 3D model by performing image view synthesis using Instant-NGP on images taken from 4 or more different angles. After that, we use DeepLabCut to build joint coordinates and skeletons. By learning the built skeleton to artificial intelligence, a pre-trained AI model is created. Using an artificial intelligence model learned in advance through a 3D model, joint coordinate recognition in a 2D image, skeleton construction, posture estimation, and classification are carried out. Through the test image dataset, the posture estimation and classification accuracy of the artificial intelligence model trained in advance with the 3D model and the artificial intelligence model learned using the existing 2D image are compared.
Structuring the benchmarks of artistic and creative motivation for creating a stage image of a role by an actor in a drama theatre

The purpose of the article. Determination of the structure of the main guidelines of artistic and creative motivation in the process of creating a stage image of a role by an actor in a drama theatre. The research methodology consists of the use of systemic, art history, analytical, structural, and functional methods of researching the main guidelines in organizing the creative process of transforming the performer of the role actor into a certain stage image in accordance with the subject of his creative activity in the theatrical production, which is directly represented by the character of the dramatic work. Research novelty. For the first time, the structuring of the conceptual and categorical basis of guidelines for artistic and creative motivation of professional requirements for creating a stage image by an actor in a drama theatre has been carried out. Conclusions. At present, the fundamental scientific-theoretical method of consistency in building the structuring of the process of an actor's work on a stage image, apart from Stanislavsky's orientation system, has not been developed. For the better or not, is a controversial issue, because the creative search for a more effective and universal basis for acting is taking place in modern creative laboratories of theatre artists. So, the process of inventing new original artistic techniques for a stage play, figurative motivations, and the interpretation of a character in a dramatic form remains an incompletely resolved problem for stage artists.&#x0D; Key words: theatre, actor, acting, stage image, character, role, character.
Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning Approach Using Clinical Note Annotations

Abstract Background Lower back pain is a common weakening condition that affects a large population. It is a leading cause of disability and lost productivity, and the associated medical costs and lost wages place a substantial burden on individuals and society. Recent advances in artificial intelligence and natural language processing have opened new opportunities for the identification and management of risk factors for lower back pain. In this paper, we propose and train a deep learning model on a data set of clinical notes that have been annotated with relevant risk factors, and we evaluate the model’s performance in identifying risk factors in new clinical notes. Objective The primary objective is to develop a novel deep learning approach to detect risk factors for underlying disease in patients presenting with lower back pain in clinical encounter notes. The secondary objective is to propose solutions to potential challenges of using deep learning and natural language processing techniques for identifying risk factors in electronic medical record free text and make practical recommendations for future research in this area. Methods We manually annotated clinical notes for the presence of six risk factors for severe underlying disease in patients presenting with lower back pain. Data were highly imbalanced, with only 12% (n=296) of the annotated notes having at least one risk factor. To address imbalanced data, a combination of semantic textual similarity and regular expressions was used to further capture notes for annotation. Further analysis was conducted to study the impact of downsampling, binary formulation of multi-label classification, and unsupervised pretraining on classification performance. Results Of 2749 labeled clinical notes, 347 exhibited at least one risk factor, while 2402 exhibited none. The initial analysis shows that downsampling the training set to equalize the ratio of clinical notes with and without risk factors improved the macro–area under the receiver operating characteristic curve (AUROC) by 2%. The Bidirectional Encoder Representations from Transformers (BERT) model improved the macro-AUROC by 15% over the traditional machine learning baseline. In experiment 2, the proposed BERT–convolutional neural network (CNN) model for longer texts improved (4% macro-AUROC) over the BERT baseline, and the multitask models are more stable for minority classes. In experiment 3, domain adaptation of BERTCNN using masked language modeling improved the macro-AUROC by 2%. Conclusions Primary care clinical notes are likely to require manipulation to perform meaningful free-text analysis. The application of BERT models for multi-label classification on downsampled annotated clinical notes is useful in detecting risk factors suggesting an indication for imaging for patients with lower back pain.
LauNuts: A Knowledge Graph to Identify and Compare Geographic Regions in the European Union

The Nomenclature of Territorial Units for Statistics (NUTS) is a classification that represents countries in the European Union (EU). It is published at intervals of several years and organized in a hierarchical system where geographical areas are subdivided according to their population sizes. In addition to NUTS, there is a further subdivided hierarchy level, named Local Administrative Units (LAU), whose data are updated annually by EU member states. While both datasets are published by Eurostat as Excel files, an additional RDF dataset is available for NUTS up to the 2016 scheme. With this work, we provide the Linked Data community with an up-to-date Knowledge Graph in which NUTS and LAU data are linked and which contains population numbers as well as area sizes. We also publish an Open Source generator software for future released versions that will naturally arise due to changes in population numbers. These contributions can be used to enrich other datasets and allow comparisons among regions in the European Union. All resources are available at https://w3id.org/launuts .
RELD: A Knowledge Graph of Relation Extraction Datasets

Relation extraction plays an important role in natural language processing. There is a wide range of available datasets that benchmark existing relation extraction approaches. However, most benchmarking datasets are provided in different formats containing specific annotation rules, thus making it difficult to conduct experiments on different types of relation extraction approaches. We present RELD, an RDF knowledge graph of eight open-licensed and publicly available relation extraction datasets. We modeled the benchmarking datasets into a single ontology that provides a unified format for data access, along with annotations required for training different types of relation extraction systems. Moreover, RELD abides by the Linked Data principles. To the best of our knowledge, RELD is the largest RDF knowledge graph of entities and relations from text, containing $$\sim $$ 1230 million triples describing 1034 relations, 2 million sentences, 3 million abstracts and 4013 documents. RELD contributes to a variety of uses in the natural language processing community, and distinctly provides unified and easy modeling of data for benchmarking relation extraction and named entity recognition models.
LIS: A Knowledge Graph-Based Line Information System

In a manufacturing enterprise, like Bosch, answering business questions regarding production lines, involves different stakeholders. Production planning, product and production process development, quality management, and purchase have different views on the same entity “production line”. These different views are reflected in data residing in silos as Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP) systems as well as Master Data (MD) systems. To answer these questions, all data have to be integrated and semantically harmonized conciliating the different views in a uniform understanding of the domain. To fulfill these requirements in this specific domain, we present the Line Information System (LIS). LIS is a Knowledge Graph (KG)-based ecosystem capable of semantically integrating data from MES, ERP, and MD. LIS enables a 360 $$^{\circ }$$ view of manufacturing data for all stakeholders involved while resolving Semantic Interoperability Conflicts (SICs) in a scalable manner. Furthermore, as a part of the LIS ecosystem, we developed the LIS ontology, mappings, and a procedure to ensure the quality of the data in the KG. The LIS application comprises many functionalities to answer business questions that were not possible without LIS. LIS is currently in use in 12 Bosch plants semantically integrating data of more than 1.100 production lines, 16.000 physical machines, as well as more than 400 manufacturing processes. After the rollout of LIS, we performed a study with 21 colleagues. In general, the study showed that LIS in particular, and KG-based solutions in general, paves the way of exploiting the knowledge in manufacturing settings in a reusable and scalable way.
Dipping my toes into 3D

This week I am a bit at a loss what to write about - I had drafts on the weaponization of meta-research, how to write an R package in 2022 (but the year's almost out), the consequences of owning your digital...
Statistical Analysis of Substation Defect Rules and Characteristics Based on Word Frequency Analysis

Predicting the future state of the equipment helps arrange targeted defect troubleshooting. Therefore, studying the rules and characteristics of substation defects is necessary. Based on word frequency analysis, this paper analyzes 18,856 defect samples collected. And the defect attention index and defect comprehensive index are proposed to quantify the defect grade. It is found that in terms of voltage levels, pay more attention to the equipment with the 110kV voltage level; in terms of equipment type, pay more attention to the main transformer, protective device, knife switch, breaker, and capacitor; in terms of month, pay more attention to the health status of equipment in April, July and December; in terms of the concentration degree of defect types, general defect > serious defect > critical defect. Compared with open-type substations, because the potential transformer, current transformer, arrester, and knife switch in the GIS substation are integrated into the GIS combined equipment, the fault proportion is significantly reduced. At the same time, the proportion of critical defects in the GIS substation decreased, but the proportion of serious defects increased, and the proportion of general defects was similar. This paper can provide data support to carry out targeted defect troubleshooting, reduce the failure rate of substation equipment, and further improve the safety and stability of the power grid.
LEADING PEDAGOGICAL APPROACHES TO SCIENTIFIC AND METHODOLOGICAL SUPPORT IN PREPARATION FOR SUBJECT OLYMPIADS AT THE UNIVERSITY

Statement of the problem. To consider and study the concept of scientific and methodological support and formulate its definition in relation to preparation for subject Olympiads, it is necessary to study the main approaches to pedagogical strategy in the process of scientific and methodological support in preparation for subject Olympiads in higher education. Results. The main pedagogical approaches that contribute to improving the quality of preparation of participants for subject Olympiads in universities are being studied. It is noted that any pedagogical activity is characterized by the presence of a clearly defined goal, personally significant for the teacher carrying out this activity, and is motivated by the motives arising from it. Competent educational activity in the process of preparing for the Olympiad in any subject can be successfully carried out only when the scientific and methodological base clearly defines and solves specific educational Olympiad tasks. Conclusion. The article discusses the theoretical and practically significant components of high-quality and competent scientific and methodological support in preparation for subject Olympiads in higher education. The authors update and present the theoretical and practical substantiations of the verified scientific and methodological regulation, designed to improve the preparation of participants in the Olympiads. The main pedagogical approaches that contribute to improving the quality of preparation of participants for subject Olympiads in universities are highlighted.
Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

Abstract Background The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process. Methods In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k -fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.
Language Report Maltese

Abstract This chapter is a highly abbreviated version of an update (Rosner and C. Borg 2022) to the META-NET White Paper on Maltese (Rosner and Joachimsen 2012). Like its predecessor, the update forms part of a series for all European Languages. Section 1 provides a brief description of the language, its national status, its general typology as a language, and its current usage in the digital sphere. Section 2 gives an overview of technologies and resources that are currently available. Finally, Section 3 frames the main shortcomings of Maltese language technology in terms of fragmentation, and offers some recommendations on how that might be reduced.
Research on English semantic intelligence analysis method based on deep learning

Semantic analysis of sentences is the research basis of automatic scoring of subjective questions in English examination, English machine translation, English automatic response, automatic acquisition of English article abstracts and so on. The research on English strong thematic structure (the so-called "split sentence") first includes yesperson’s "shift analysis" and "insertion analysis", followed by Halliday’s "syntactic analysis", "theme Rheme Structure Analysis" and "participant role analysis" And other methods. The intelligent algorithm of sentence component oriented English semantic analysis can not only reduce the workload of teachers, but also avoid the inconsistent scores of students' subjective questions caused by different teachers' subjective cognition; With the continuous improvement of regular expressions and vocabulary databases of English vocabulary and grammar, the algorithm will be trusted by more and more teachers. This paper adopts the method of deep learning. Simply emphasizing shallow learning of knowledge can no longer meet the requirements of modern society for knowledge. Deep learning is a method based on the understanding of knowledge, aimed at enhancing students' problem-solving and knowledge transfer abilities.