Reviewerly/exHarmony huggingface dataset:
Hypergraph Contrastive Learning for Electronic Health Records

Electronic Health Records (EHR) is the repository of patients' involved medical codes in the hospital, including diagnosis codes, medication codes, procedure codes, lab codes, and so on. EHR inherently contains various kinds of relationships such as the code-code, the patient-patient, and the patient-code relationship. Recent research shows that graph representation learning can be an effective tool for capturing complex relationships. However, none of the existing methods considered high-order interactions between patients and medical codes or considered the three relationships together. In this paper, we propose Hypergraph Contrastive Learning (HCL), to jointly learn patient embeddings and code embeddings from the combination of the above three relationships. HCL first constructs a hypergraph from the EHR data. Then, the medical code graph and the patient graph are constructed based on the hypergraph. Empowered with hypergraph attention network, Transformer, and graph attention network, HCL learns representations from three graphs respectively. Next, contrastive learning is applied to aggregate information from these graphs. Finally, the learned representations can support downstream tasks in supervised learning settings and self-supervised learning settings. Experiments are conducted on eICU and MIMIC-III datasets with mortality prediction and readmission prediction tasks. Results show that our method outperforms almost all compared methods on all evaluation metrics and HCL can learn patient representations from medical codes even without labeled data.
Constrained Mean Shift Clustering

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Constrained Mean Shift ClusteringMaximilian Schier, Christoph Reinders, and Bodo RosenhahnMaximilian Schier, Christoph Reinders, and Bodo Rosenhahnpp.235 - 243Chapter DOI:https://doi.org/10.1137/1.9781611977172.27PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we present Constrained Mean Shift (CMS), a novel approach for mean shift clustering under sparse supervision using cannot-link constraints. The constraints provide a guidance in constrained clustering indicating that the respective pair should not be assigned to the same cluster. Our method introduces a density-based integration of the constraints to generate individual distributions of the sampling points per cluster. We also alleviate the (in general very sensitive) mean shift bandwidth parameter by proposing an adaptive bandwidth adjustment which is especially useful for clustering imbalanced data sets. Several experiments show that our approach achieves better performance compared to state-of-the-art methods both clustering synthetic data sets as well as clustering encoded features of real-world image data sets. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Reconfiguring for Agility: Examining the Performance Implications of Project Team Autonomy through an Organizational Policy Experiment

Agile software development, a paradigm that emphasizes project team autonomy and the value of responding to changes over following standardized processes, has gained prominence in the software industry. Prior investigations on the adoptions of agile paradigms for software operations and their performance implications have typically focused on isolated aspects of software development processes. In this study, we adopt a configurational perspective of software operations and assess the causal impacts of adopting an organizational policy that grants higher levels of autonomy to project teams. Building on the equifinality framework proposed in organizational studies, we posit that an organizational policy that provides higher levels of autonomy for software teams engenders performance-enhancing adaptations through agile reconfigurations of project operations. To test our hypothesis, we collaborated with a commercial software firm and collected data from a policy experiment at the firm. We examined project-level data spanning a four-year observation period during which the firm implemented a new policy that significantly reduced the hurdles for project teams to autonomously reconfigure their operations. The results support our postulation and shed light on how an organizational policy that provides greater autonomy to software
Modeling cyberattacks with extended Petri nets

A research program consisting of several interconnected research projects in cybersecurity modeling is described. First, a public database of known cyberattack patterns is automatically processed to generate executable cyberattack component models, one for each attack pattern. The models are expressed using a form of Petri nets extended with additional features specific to modeling cyberattacks, including representations of the attacker and defender, their strategies, and the cost of their actions. Second, because the cyberattack pattern database used in the first project to generate the cyberattack component models describes the attacks in an attacker-centric manner, the cyberattack component models are enhanced to include defender actions and responses and representations of normal user activities on the computer system. Third, subsets of the cyberattack component models that have been previously generated and stored in a repository are automatically selected and composed (linked) to form a complete model of a particular target computer system. Fourth, the assembled composite cyberattack model is verified and validated as an accurate model of the specific cyberattacks and the target computer system. This process uses application-relevant verification and validation methodologies. Finally, the validated cyberattack model is executed in order to simulate cyberattacks on the target computer system. Multiple simulation iterations are used to drive reinforcement learning methods that automatically improve strategies to attack or defend the target computer system.
Federated Learning with Noisy User Feedback

Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Self-adaptive bee colony optimisation algorithm for the flexible job-shop scheduling problem

The bee colony optimisation (BCO) algorithm is a nature-inspired algorithm that models the natural behaviour of honey bees as they find nectar and share food sources information with other bees in the hive. This paper presents the BCO algorithm for the flexible job-shop scheduling problem (FJSP), furthermore, to improve the neighbourhood search in the BCO algorithm we introduce a self-adaptive mechanism to the BCO algorithm (self-adaptive-BCO algorithm) for adaptively selecting the neighbourhood structure to enhance the local intensification capability of the algorithm and to help the algorithm to escape from a local optimum. We perform computational experiments on three well-known benchmarks for FJSP. The BCO algorithm is compared with the self-adaptive-BCO algorithm to test the performance of the latter. The results demonstrate that the self-adaptive-BCO algorithm outperforms the BCO algorithm, the proposed approach also outperforms the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets.
The ways to improve the quality of training in higher education based on the analysis of students' attitudes towards extracurricular unsupervised activities

О. В. Тихонова, И. А. Азизян, Н
College English teaching mode based on intelligent robot

Using the traditional method to design college English teaching mode, normally the students' satisfaction is not high, which leads to the situation where the students' performance is not ideal. Therefore, a research method of college English teaching mode based on intelligent robots is proposed. Firstly, in order to realise the stable control of the intelligent robot in college English teaching, a motion planning model of the intelligent robot in college English teaching is constructed. Secondly, the control design of college English teaching machine is optimised. A specific improvement method to the college English teaching mode based on intelligent robots is proposed. Finally, the college English teaching mode based on intelligent robot is realised. According to the experimental results, the proposed method is significantly superior to the traditional method in terms of enhancement effect of students satisfaction and performance, indicating that the proposed method has high application value and certain reference value.
Updatable Blockchains

Software updates for blockchain systems become a real challenge when they impact the underlying consensus mechanism. The activation of such changes might jeopardize the integrity of the blockchain by resulting in chain splits. Moreover, the software update process should be handed over to the community and this means that the blockchain should support updates without relying on a trusted party. In this paper, we introduce the notion of updatable blockchains and show how to construct blockchains that satisfy this definition. Informally, an updatable blockchain is a secure blockchain and in addition it allows to update its protocol preserving the history of the chain. In this work, we focus only on the processes that allow securely switching from one blockchain protocol to another assuming that the blockchain protocols are correct. That is, we do not aim at providing a mechanism that allows reaching consensus on what is the code of the new blockchain protocol. We just assume that such a mechanism exists (like the one proposed in NDSS 2019 by Zhang et al.), and show how to securely go from the old protocol to the new one. The contribution of this paper can be summarized as follows. We provide the first formal definition of updatable ledgers and propose the description of two compilers. These compilers take a blockchain and turn it into an updatable blockchain. The first compiler requires the structure of the current and the updated blockchain to be very similar (only the structure of the blocks can be different) but it allows for an update process more simple, efficient. The second compiler that we propose is very generic (i.e., makes few assumptions on the similarities between the structure of the current blockchain and the update blockchain). The drawback of this compiler is that it requires the new blockchain to be resilient against a specific adversarial behaviour and requires all the honest parties to be online during the update process. However, we show how to get rid of the latest requirement (the honest parties being online during the update) in the case of proof-of-work and proof-of-stake ledgers.
Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com

Abstract In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.
Phase response measurement of spatial light modulators based on a Shack–Hartmann wavefront sensor

It is known that the phase response of spatial light modulators (SLMs) measured by double-beam interferometers is sensitive to mechanical and environmental disturbances. This paper proposes a Shack–Hartmann wavefront sensor (SHWS) method to measure the phase response characteristics of the SLM. The results show that the phase modulation depth measured by the proposed method is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1.7581</mml:mn> </mml:mrow> <mml:mi>λ<!-- λ --></mml:mi> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1.7993</mml:mn> </mml:mrow> <mml:mi>λ<!-- λ --></mml:mi> </mml:math> by the Twyman–Green interferometer method. The difference in the phase modulation depth between the two methods is only <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>0.0412</mml:mn> </mml:mrow> <mml:mi>λ<!-- λ --></mml:mi> </mml:math> , and its relative error rate is 2.29%. It proves that the phase modulation accuracy obtained by the SHWS with lenslets of 73*73 used in this paper is equivalent to that of the Twyman–Green interferometer. Compared with the interferometer method, the SHWS method is simple, compact, and robust, has good real-time performance, and is relatively vibration insensitive. In the future, the SHWS method will play a more important role in the detection of the SLM’s phase response.
Systematic Literature Review: Improving Self Regulated Learning Through The Flipped Classroom Model Based on Interactive E-Books

In the flipped classroom, students must have independent learning behavior to achieve optimal competence. However, the reality is that students' independence in learning is not good, so a teacher is supposed to carry out knowledge that can facilitate the formation of student learning independence. One of the lessons that can increase students' learning independence is the Flipped Classroom model with the help of interactive e-books. This study aims to conduct a literature review on the importance of improving student learning independence through the Flipped Classroom model based on Interactive e-books. The SLR (Systematic Literature Review) method was chosen in this study. Data collection was carried out by documenting and reviewing all articles on learning independence that had similar research in this report. A total of 15 journal articles obtained from Google Scholar with the help of the publish and perish application were used in this study. Based on the literature review, it can be concluded that increasing the ability to learn independently is needed by students in learning, especially in online education with the interactive e-book-based flipped classroom model. Independent learning has a positive influence on students in education.
Detection of Power Line Insulator Based on Enhanced YOLO Model

As a special insulation device, insulators play an important role in aerial transmission lines. It can be easily influenced by the wind load, snow load and wire swing in severe weather. The fault of the insulator will seriously affect and destroy the safety and normal operation of the entire transmission line. Therefore, in the operation and inspection of the transmission line, the detection of insulator defects must be strengthened. Since the insulators are constructed in a complex environment and the background of the images collected by air is also extremely complicated, traditional defect detection methods rely on specific scenarios for specific analysis and the robustness is weak. Changes in scenes, changes in UAV&#x2019;s shooting angles, and changes in lighting conditions will all make it difficult for the model to get good inspection results. This paper proposes an algorithm for locating and detecting insulator defects based on an enhanced YOLO model. Due to the electromagnetic interference during the power inspection of the UAV, there is a certain amount of noise in the image of the aerial insulator. At the same time, in order to solve the problem of few shots which leads to training difficulty, this paper uses a variety of data augmentation, including affine transformation, Gaussian blur processing, gray scale transformation, brightness and contrast transformation and random erasing of pixels, and then the augmentation data is used to train different YOLO models. The results show that the detection algorithm based on the YOLO model meets the robustness and accuracy of insulator defect detection, and augmented experiment results are better than those of the conventional YOLOs in power line insulator defect detection tasks.
AI and Big Data in Water Environments

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAI and Big Data in Water EnvironmentsHuichun ZhangHuichun ZhangMore by Huichun ZhangView Biographyhttps://orcid.org/0000-0002-5683-5117Cite this: ACS EST Water 2022, 2, 6, 904–906Publication Date (Web):May 25, 2022Publication History Received10 May 2022Published online25 May 2022Published inissue 10 June 2022https://doi.org/10.1021/acsestwater.2c00203Copyright © 2022 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views370Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertsSUBJECTS:Algorithms,Drinking water,Manufacturing,Nanoparticles,Water treatment Get e-Alerts
Transformational learning of teachers

The authors of the article consider transformational education as a formation of sustainable professional development. Its appearance is associated with attempts to resolve many crises states of society, which indicate the destructive phenomena of any society, regardless of its socio-economic formation and political model of social development. Many scientists recognize that a more sustainable model of human development can be achieved through education. However, the classical learning model, which has shown its effectiveness in the past centuries, is now experiencing powerful pressure from modern information technologies. This article introduces readers to its main and universal theoretical and research positions. Since the practice of implementing transformational learning can vary not only depending on the conceptual approaches, but also the basic cultural features of the country implementing it. The article describes the results of transformational professional development of teachers in Russia and Kazakhstan, proving its effectiveness.
<i>Erratum to</i> Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru

Correction| May 26, 2022 Erratum to Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru Laure Fallou; Laure Fallou * 1European‐Mediterranean Seismological Centre, Arpajon, France *Corresponding author: Fallou@emsc-csem.org https://orcid.org/0000-0002-0245-6197 Search for other works by this author on: GSW Google Scholar Francesco Finazzi; Francesco Finazzi 2Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, Italy https://orcid.org/0000-0002-1295-7657 Search for other works by this author on: GSW Google Scholar Rémy Bossu Rémy Bossu 1European‐Mediterranean Seismological Centre, Arpajon, France3CEA, DAM, DIF, Arpajon, France https://orcid.org/0000-0002-9927-9122 Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2022) 93 (4): 2410. https://doi.org/10.1785/0220220132 Article history first online: 26 May 2022 Connected Content Errata: Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Laure Fallou, Francesco Finazzi, Rémy Bossu; Erratum to Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru. Seismological Research Letters 2022;; 93 (4): 2410. doi: https://doi.org/10.1785/0220220132 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search In the original version of the article, authors’ first and last names were inverted. This version contains the corrected authors’ names. The appropriate quote for the article is therefore: Fallou, L., F. Finazzi, and R. Bossu (2022). Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru, Seismol. Res. Lett.93, no. 2A, 827–839, doi: 10.1785/0220210233. The article can be cited as “Fallou et al. (2022)” in communications. The authors acknowledge that there are no conflicts of interest recorded. View Original Article You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
BGNN: Detection of BGP Anomalies Using Graph Neural Networks

The Border Gateway Protocol (BGP) builds the communication routes at the Internet scale. Anomalies in BGP have several causes and can impact the Internet stability. BGP data traces are complex and require specific methods such as machine learning to be processed for anomaly detection. Two types of features are used to study large scale events with machine learning models: graph features or statistical features. Despite the recent interest for the concept of Graph Neural Network (GNN), there is no proposal that adapts GNN for BGP anomaly detection directly from the BGP graph. In this paper, we propose BGNN, a GNN model which detects if a node is involved in a large scale BGP anomaly. Our results show a maximum accuracy of 96% and the model can detect an anomaly after 6 minutes with 90% accuracy. These results are promising and suggest GNN for BGP anomaly detection are worth investigating,