Reviewerly/exHarmony huggingface dataset:
Dual Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-Identification

Although clustering-based unsupervised domain adaptive person re-identification has achieved promising progress, negative influence of noisy pseudo labels is still unsolved. Training model with noisy labeled data will mislead feature representation and ultimately affect the improvement of model performance. To tackle the above problem, we propose a dual pseudo label refinement framework for unsupervised domain adaptive person re-identification. It has two pseudo label refinement modules, one learns cross consistency of corresponding features between two collaborative networks and the other explores mutual consistency between global and local feature spaces. By working complementarily and jointly, the two modules optimize the quality of training datatset. Specifically, a mean teacher-student framework with multiple branches is designed to extract global and local features. Cross consistency of corresponding features between mean teacher and student networks is learned to select candidate samples for training. Meanwhile, mutual consistency of global and local features in mean teacher network is also explored to select other candidates. Finally, the intersection of two selected candidate datasets is used to re-train the framework. Being trained with selected reliable samples iteratively, the framework becomes more and more effective and robust. Extensive results on benchmark datasets confirm that our dual pseudo label refinement brings performance improvement and outperforms the state-of-the-art clustering-based unsupervised domain adaptive re-identification approaches.
Implementation of the Index Card Match Method to Increase Students' Interest in Class X ICT Subject at SMK Ibnu Kholdun Al-Hasyimi Even Semester for Academic Year 2020/2021

According to field observations conducted at the Ibn Kholdun Al-Hasyimi Vocational School, it was found that learning still uses the dominant conventional method with the lecture method in explaining the material, students learn with textbooks, and do exercises from student worksheets. The teacher has not used learning media that is in accordance with the complexity of the material presented. Understanding of the concept of learning materials is still low, feelings of liking for learning situations, feelings of liking for student activities and student activity in class during the learning process. Learning in class X SMK Ibnu Kholdun Al-Hasyimi is still generally with lecture method learning, so students are less enthusiastic in accepting learning and cause boredom. Besides this, conditions that do not support learning often occur, students' attention is also low, in the teaching and learning process students are less enthusiastic because learning makes students inactive. Based on the above background, in this study the problem raised is whether the application of the Index Card Match method to increase student interest in learning for class X students in Information and Communication Technology subjects at Ibnu Kholdun Al-Hasyimi Vocational School in the even semester of the 2020/2021 academic year? The research design uses classroom action research. The research was determined by the location at the Ibn Kholdun Al-Hasyimi Vocational School. The location of the research was determined by Ibn Kholdun Al-Hasyimi Widoropayung, which is located at Jalan KP. School RT/RW 02/01 Widoropayung Village Besuki Situbondo. Data collection is observation, interview and documentation. Data analysis Analysis of students' interest in learning basic programming questionnaire data. Based on the results of the research and discussion, it can be concluded as follows: there is an increase in student interest in learning reaching 30% from 65% of the first cycle, an increase of 30% in the second cycle of class X subjects of Information and Communication Technology at Ibnu Kholdun Al-Hasyimi Vocational School in the even semester of the 2020/2021 academic year.
Modeling of Electronic Health Records for Time-Variant Event Learning Beyond Bio-Markers—A Case Study in Prostate Cancer

Electronic health records (EHR) of large populations constitute a vast untapped resource for data-driven diagnosis and disease progression. We develop a model capable of predicting future steps in a patient’s journey for prostate cancer (PC) and its metastases without relying on direct biomarker-measurements on a set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18\,529$ </tex-math></inline-formula> EHR. To this end, we 1) harmonise EHR without presumptions–events are sorted and grouped by fundamental a priori principles; 2) develop a new Long-Short-Term Memory (LSTM) recurrent neural network node for learning temporal relations, on which we build an autoencoder based model; 3) derive a graph representation based on unsupervised <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering of events related to PC in the autoencoder’s latent layer. We report <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$88 {\%}$ </tex-math></inline-formula> predicting accuracy for the targeted metastasis-related events, and lower accuracies for more general events. The model gains interpretability with a graph representation illustrating the patient journey. Most importantly, we predict that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20 {\%}$ </tex-math></inline-formula> of all PC diagnosed patients will progress into metastatic disease one visit ahead of time. For the remaining patients we can predict the next step in their journey. We conclude that the model based on the new LSTM node provides a valuable tool for earlier diagnosis of life threatening metastases and quality assurance of the procedure.
Empowering the Battle Against COVID-19: Exploring the Impact of Artificial Intelligence Solutions

Objective: Artificial intelligence is becoming increasingly important for social purposes, and a new form of governance known as algorithmic governance is emerging. However, there is a need to prevent the abuse of market power and the rise of monopolies. In the use of AI for combatting the COVID-19 pandemic, it is essential to develop frameworks that overcome the inefficiency of ethical and legal principles to avoid ethical laundering. This paper aims to analyze existing regulations for the use of AI and propose frameworks for the analysis of legal and ethical restrictions to control the COVID-19 pandemic.&#x0D; Methodology: This paper employs a combination of national and international theoretical research methods to promote dialogue between various fields of knowledge and an interdisciplinary perspective.&#x0D; Results: The study proposes the need for hetero-regulation, regulated self-regulation, and self-regulation in the use of AI to control the COVID-19 pandemic. It suggests that frameworks for the analysis of legal and ethical restrictions should be developed to overcome critical problems and improve current strategies for using AI.&#x0D; Contributions: This paper provides possible solutions and measures to address critical problems concerning the use of AI in the fight against the COVID-19 pandemic. It highlights the need for regulatory frameworks that promote ethical and legal principles to ensure that AI is used effectively and responsibly.
Language, Syntax, and Semantics for Describing Dynamics of Systems

Natural language, or ordinary language, is any systemSystem of communication that has evolved in humans through use and repetition without conscious planning or premeditation. Its purpose typically is that we can interact in more sophisticated ways than, say, wolves when hunting or bees when collecting nectar or fish when schooling. Natural languages are extremely flexible and so can be used for everything we are capable of interacting, communicating, and expressing. Yet in some domains, using natural language was found to be complicated or awkward, so our ancestors invented special-purpose languages. These languages include not only mathematical notation, logical representation, and computer language but also musical notation. They have one common element: they are expressed and perceived visually rather than auditory—so-called semasiographic languagesSemasiographic language. Like natural languages, each has its specific syntax and semantics—so does the language of stock and flow diagrams or SFD. Before one can use SFDSFDStock and Flow Diagram, one first has to learn it. That is what this chapter is about.
Augmented Reality Enabling Better Education

Training is a significant part of every medical services framework. Patients should be trained in their arranged methodology. The medical services experts should be prepared for their separate callings. The patient schooling and the preparation of medical care experts are face-to-face, which requires assets and is bound to specific environments. The previous methodology parts the perception plan into four levels. The issue portrayal and deliberation depend on the planned concentrate on approach, which carries us to the main level of the settled model. Diverse works are within the accompanying watchword blends connecting with the study. The framework has various pictures. The patient lies in a warped place, and a revision of their position is done. The gantry moves to its underlying location. The gantry gadget moves back to its unique position. The table dives so the patient can get up and leave the room. VIPER was furnished with sound. The term of the entire data movement was 2:45 min.
Training for Better Transfer in an Online Competency-Based Higher Education Program

Virtual reality simulations represent a much-needed effort to move beyond the shortcomings of traditional form-based assessments. Within VR, we assess competency and problem-solving skills versus the content memorization typically supported by conventional measures. This chapter explores an innovative VR simulation recently deployed at Western Governors University. The authors explored the utility of a VR simulation as an assessment tool when students engaged in more inclusive, immersive, and interactive experiences compared to conventional methods. The authors investigated students' summative assessment scores across a 2D (desktop) and 3D VR (headset) version and how additional factors like motion sickness, cognitive workload, and system usability impacted their scores. The results showed that students in the Desktop version outperformed those in the VR version on the summative assessment while feeling equally immersed in the simulation. Implications for future research are discussed, especially for optimizing learning experiences in an online competency-based higher education program.
Using UX Design Principles When Designing Context-Rich Learning Experiences

To make the most context-rich experiences possible and avoid a myriad of problems, designers must keep a user-centric focus. By viewing our educational products through the perspective of the user, we can devise more accessible, diverse, meaningful, and above all enjoyable experiences. Using simple tools such as problem statements, personas, user maps, story boarding, wireframes, low-definition prototypes, and high-definition prototypes, we can consult users first in design to inform us on how to proceed with decisions. Using the simple outlined in the Design Thinking process of empathizing, defining, ideating, prototyping, and testing, we can deliver the most user centric experience possible. The author will show how Design Thinking couples so well with User Experience (UX), walk through what a user study is, what to do with the feedback you get from users, and how to keep the user in mind from inception to launch. In this chapter, the author will discuss the basic framework of UX, where to apply the tools within the framework and why these tools are important when developing context-rich experiences.
A Caching-Enabled Permissioned Blockchain Scheme for Industrial Internet of Things Based on Deep Reinforcement Learning

The integration of the industrial internet of things (IIoT) and blockchain has become a popular concept that provides IIoT with a trustworthy computing environment. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can offer great data processing capabilities and low-latency services. However, we still face the challenges of how to efficiently process the massive IIoT data on resource-constrained IIoT nodes by blockchain smart contracts, as their storage capacity only allows them to store limited blockchain data. This work is aimed at improving the smart contract execution efficiency on these IIoT nodes by caching based on deep reinforcement learning. On the one hand, focusing on the characteristics of IIoT, the ledger structure, network architecture, and transaction flow are optimized. IIoT nodes are enabled to store and cache part of block data without affecting global data consistency. On the other hand, we formulated the blockchain caching problem as a Markov decision process and implemented a lightweight caching agent based on deep Q-learning. Proper features and a reward function are defined to minimize the execution delay of smart contracts. The extensive experimental results show that our proposed scheme can effectively reduce the data dissemination costs and smart contract execution delays of IIoT nodes that hold limited blockchain data.
Clustering in Bitcoin balance

This study investigates clustering in Bitcoin wallet balances by examining the Bitcoin rich list. We discover significant clustering in both integer and fractional parts of balances, particularly at 00. Using probit models, we find that wallet age, number of transactions, and balance significantly impact clustering, with balance being the most influential factor. Our findings suggest that economic and behavioral factors may drive Bitcoin wallet holders’ preferences, contributing to the observed clustering. This study offers valuable insights into the behavior of Bitcoin users and sets the stage for further investigation in this area.
Measuring language complexity: challenges and opportunities

Abstract This special issue focuses on measuring language complexity. The contributions address methodological challenges, discuss implications for theoretical research, and use complexity measurements for testing theoretical claims. In this introductory article, we explain what knowledge can be gained from quantifying complexity. We then describe a workshop and a shared task which were our attempt to develop a systematic approach to the challenge of finding appropriate and valid measures, and which inspired this special issue. We summarize the contributions focusing on the findings which can be related to the most prominent debates in linguistic complexity research.
Measuring the Schmidt number of parametric down conversion by exploiting photon distribution

The Schmidt number quantifies the number of modes and is mainly used as a measure for the quality of entanglement. We theoretically compute the photon distribution of type-I spontaneous parametric down conversion (SPDC) with an arbitrary Schmidt number. The photon distribution is used for a novel method to measure the Schmidt number. This method requires only two on–off single-photon detectors with no photon number or temporal resolution. The method works in the strong pumping regime where high photon numbers are non-negligible. We experimentally demonstrate the method for type-II SPDC. The easy and fast measurement of the Schmidt number has a broad range of applications from the calibration of strong pump SPDC and entanglement sources to multi-photon quantum interference and Gaussian boson sampling.
Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy

The overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker in therapeutic effects for metastatic breast cancer. Accurate HER2 testing is critical for determining the most suitable treatment for patients. Fluorescent in situ hybridization (FISH) and dual in situ hybridization (DISH) have been recognized as FDA-approved methods to determine HER2 overexpression. However, analysis of HER2 overexpression is challenging. Firstly, the boundaries of cells are often unclear and blurry, with large variations in cell shapes and signals, making it challenging to identify the precise areas of HER2-related cells. Secondly, the use of sparsely labeled data, where some unlabeled HER2-related cells are classified as background, can significantly confuse fully supervised AI learning and result in unsatisfactory model outcomes. In this study, we present a weakly supervised Cascade R-CNN (W-CRCNN) model to automatically detect HER2 overexpression in HER2 DISH and FISH images acquired from clinical breast cancer samples. The experimental results demonstrate that the proposed W-CRCNN achieves excellent results in identification of HER2 amplification in three datasets, including two DISH datasets and a FISH dataset. For the FISH dataset, the proposed W-CRCNN achieves an accuracy of 0.970±0.022, precision of 0.974±0.028, recall of 0.917±0.065, F1-score of 0.943±0.042 and Jaccard Index of 0.899±0.073. For DISH datasets, the proposed W-CRCNN achieves an accuracy of 0.971±0.024, precision of 0.969±0.015, recall of 0.925±0.020, F1-score of 0.947±0.036 and Jaccard Index of 0.884±0.103 for dataset 1, and an accuracy of 0.978±0.011, precision of 0.975±0.011, recall of 0.918±0.038, F1-score of 0.946±0.030 and Jaccard Index of 0.884±0.052 for dataset 2, respectively. In comparison with the benchmark methods, the proposed W-CRCNN significantly outperforms all the benchmark approaches in identification of HER2 overexpression in FISH and DISH datasets (p<0.05). With the high degree of accuracy, precision and recall , the results show that the proposed method in DISH analysis for assessment of HER2 overexpression in breast cancer patients has significant potential to assist precision medicine.
Probabilistic Properties of Zipf Sets and Their Maximal Intersections

In the article, the weak and strong laws of large numbers are obtained for various characteristics of a generalized Zipf set. Based on these results, we investigate the maximal intersection size between a random Zipf set and the elements of a large base of independent random sets of the same type but, eventually, with different parameters. This question is significant for applications.
Exploring the knowledge certainty shift: Metaknowledge analysis on drugs via assertion uncertainty burstiness

Pharmacology remains at the center of attention in our effort to ensure everyone has the opportunity for a better life. Previous works underline several contributions of pharmaceutical innovation to economic and social aspects. Based on such importance, numerous studies have conducted thorough metaknowledge analyses of current findings related to drugs using various information. Although there are a few notable works in mapping current findings and generating hypotheses of drugs, the certainty level of findings is lightly explored. Certainties are usually expressed in the literature using hedge cues and indicating speculations. Qualifying certainty expressed in published literature is essential in facilitating the precision of its findings and projecting research directions. This paper suggests a novel Assertion Uncertainty Burstiness Analysis Framework (AUBAF) for capturing and quantifying uncertainty embedded in pharmacological literature. The suggested approach categorizes drugs based on uncertainty burstiness, enabling a diachronic analysis of assertion uncertainty shift. We conducted experiments using literature on drugs commonly used in neurodegenerative disease treatment and concluded that AUBAF could determine and interpret uncertainty patterns. Our experiment results also highlight several drugs with high uncertainty levels, even if they have been approved and commercialized for a particular time. We believe AUBAF and our experiment results can stimulate more comprehensive research on neurodegenerative disease treatments and contribute to clinical practice in the long run.
CredsCache: Making OverlayFS scalable for containerized services

OverlayFS, a Linux kernel driver for the Docker container, supports access control in Linux. User processes in the container must have appropriate privileges to access files in the backing file system. To that end, OverlayFS temporarily overrides credentials of a process with the mounter's credentials whenever the process accesses a file, inode, or directory. However, we found that this mechanism incurs severe overhead owing to contention in updating a shared reference counter for the mounter's credentials, which hinders OverlayFS scalability. In this paper, we propose a credential caching scheme, CredsCache, which greatly relieves contention by maintaining a per-process cached version of the mounter's credentials. In CredsCache, each process performs credential overriding and reverting using its own cached version without updating the shared reference counter. We implemented CredsCache in OverlayFS and evaluated its performance using micro- and macro-benchmarks. Using solid-state drive (SSD) backend storage, the micro-benchmark results showed that CredsCache achieved up to 23.3× and 54.7× higher performance for data and metadata I/O, respectively, than vanilla OverlayFS. Moreover, the macro-benchmark and real-world benchmark results showed that CredsCache produced up to 5.6× and 2.9× better performance for respective database and web server benchmarks than vanilla OverlayFS.
Classroomscreen

Die Unterrichtspraxis/Teaching GermanEarly View INVITED REVIEW Classroomscreen Beth A. Burau, Corresponding Author Beth A. Burau [email protected] orcid.org/0000-0003-3237-8350 Instructional Technology and World Language Department, Bishop Lynch High School, Dallas, Texas, USA Correspondence Beth A. Burau, Instructional Technology and World Language Department, Bishop Lynch High School, Dallas, TX, USA. Email: [email protected]Search for more papers by this author Beth A. Burau, Corresponding Author Beth A. Burau [email protected] orcid.org/0000-0003-3237-8350 Instructional Technology and World Language Department, Bishop Lynch High School, Dallas, Texas, USA Correspondence Beth A. Burau, Instructional Technology and World Language Department, Bishop Lynch High School, Dallas, TX, USA. Email: [email protected]Search for more papers by this author First published: 04 May 2023 https://doi.org/10.1111/tger.12221 Free Basic version; $29.90/year Pro version; School Pro version price varies by number of licenses, platform: Web-based, https://classroomscreen.com/ Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation