Reviewerly/exHarmony huggingface dataset:
Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed types of tissue is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure -- zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on a public breast cancer and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms -- achieving a substantial improvement of up to $17\%$ on Dice score. The code is publicly available at: https://github.com/tio-ikim/valuing-vicinity
3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections

3D human pose estimation in multi-view operating room (OR) videos is a relevant asset for person tracking and action recognition. However, the surgical environment makes it challenging to find poses due to sterile clothing, frequent occlusions, and limited public data. Methods specifically designed for the OR are generally based on the fusion of detected poses in multiple camera views. Typically, a 2D pose estimator such as a convolutional neural network (CNN) detects joint locations. Then, the detected joint locations are projected to 3D and fused over all camera views. However, accurate detection in 2D does not guarantee accurate localisation in 3D space. In this work, we propose to directly optimise for localisation in 3D by training 2D CNNs end-to-end based on a 3D loss that is backpropagated through each camera's projection parameters. Using videos from the MVOR dataset, we show that this end-to-end approach outperforms optimisation in 2D space.
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning

Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces a number of technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. Our framework eliminates the major technical obstacles to predictive modeling with machine learning that currently impede clinical adoption. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide. In addition, AutoPrognosis 2.0 is provided as an open-source python package. By open-sourcing our framework as a tool for the community, clinicians and other medical practitioners will be able to readily develop new risk scores, personalized diagnostics, and prognostics using modern machine learning techniques.
Promoting and supporting of Open Science at the National Library of the Republic of Moldova through open institutional platforms

The article reflects the practice of the National Library of the Republic of Moldova (NLRM) in promoting and supporting the implementation of the paradigms of Open Access and Open Science in the context in which today, the phenomenon / movement of Open Access has encouraged more libraries, universities and research institutions to create and develop products to promote scientific results online. In this sense, the emphasis is on capitalizing on the digital library (http://www.moldavica.bnrm.md/) and the national thematic repository (http://moldlis.bnrm.md/), as open platforms that can ensure a sustainable infrastructure for seamless access to free knowledge, digital content and information resources. In this sense, the author describes NLRM's experience in using 2 digital platforms / software (DSpace and Greenstone) that are part of the open source domain and represent computer systems capable of managing, transmitting, publishing, modifying and downloading digital content in various forms and formats of accessibility. The paper aims to promote a favorable environment for further support of the open movement by the NLRM by committing to disseminating the results of research and successful experiences in the field of Library and Information Sciences (BSI), by publishing articles in the journal „Magazin Bibliologic” and placing in National Thematic Repository of Library Science and Information Sciences of the Republic of Moldova (MOLDLIS) (http://moldlis.bnrm.md/).
Immune Logics ain't that Immune

Da Ré and Szmuc argue that while there is a symmetry between ‘infectious’ and ‘immune’ logics, this symmetry fails w.r.t. extending an algebra with an immune or an infectious element. In this paper, I show that the symmetry also fails w.r.t. defining a new logical operation from a given set of primitive (Boolean) operations. I use the case of the material conditional to illustrate this point.
No-Reference Quality Assessment of Stereoscopic Video Based on Temporal Adaptive Model for Improved Visual Communication

An objective stereo video quality assessment (SVQA) strives to be consistent with human visual perception while ensuring a low time and labor cost of evaluation. The temporal–spatial characteristics of video make the data processing volume of quality evaluation surge, making an SVQA more challenging. Aiming at the effect of distortion on the stereoscopic temporal domain, a stereo video quality assessment method based on the temporal–spatial relation is proposed in this paper. Specifically, a temporal adaptive model (TAM) for a video is established to describe the space–time domain of the video from both local and global levels. This model can be easily embedded into any 2D CNN backbone network. Compared with the improved model based on 3D CNN, this model has obvious advantages in operating efficiency. Experimental results on NAMA3DS1-COSPAD1 database, WaterlooIVC 3D Video Phase I database, QI-SVQA database and SIAT depth quality database show that the model has excellent performance.
CCTV Camera Array for the Displacement and Strain Measurement of a Beam Specimen in a Laboratory

The available conventional sensors, like displacement transducers, used in the Structural Engineering Laboratory are expensive. In addition to that, the need for data acquisition devices also escalates the expense invested in point contact measurement systems. The recent appeal of vision-based measurement and the search for cost-effectiveness has led to an exploration of the established sampling moiré method using cheap closed-circuit television (CCTV) cameras as a viable option. The sampling moiré method is simple and analyzes the displacements from grating images by a phase-shifting method. Several trial experiments were conducted, which demonstrated that the setup is at least as accurate as the traditional measuring system. An experiment was conducted on a steel I-beam for displacement measurement, which displayed satisfactory results. In addition, the setup was also tested for strain measurement, and it has yielded promising results that need fine-tuning. This paper discusses the challenges, findings, and the possibility of incorporating vision-based displacement measurements in laboratory platforms.
EDLaaS:Fully Homomorphic Encryption over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE-compatible neural networks with our own open-source framework and reproducible examples. We use the fourth generation Cheon, Kim, Kim, and Song (CKKS) FHE scheme over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL). We significantly enhance the usability and applicability of FHE in deep learning contexts, with a focus on the constituent graphs, traversal, and optimisation. We find that FHE is not a panacea for all privacy-preserving machine learning (PPML) problems and that certain limitations still remain, such as model training. However, we also find that in certain contexts FHE is well-suited for computing completely private predictions with neural networks. The ability to privately compute sensitive problems more easily while lowering the barriers to entry can allow otherwise too-sensitive fields to begin advantaging themselves of performant third-party neural networks. Lastly, we show how encrypted deep learning can be applied to a sensitive real-world problem in agri-food, i.e., strawberry yield forecasting, demonstrating competitive performance. We argue that the adoption of encrypted deep learning methods at scale could allow for a greater adoption of deep learning methodologies where privacy concerns exist, hence having a large positive potential impact within the agri-food sector and its journey to net zero.
Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism

It is possible to better provide the security of the codebase and keep testing efforts at a minimum level by detecting vulnerable codes early in the course of software development. We assume that nature-inspired metaheuristic optimization algorithms can obtain “optimized patterns” from vulnerabilities created in an artificial manner. This study aims to use nature-inspired optimization algorithms combining heterogeneous data sources with the objective of learning optimized representations of vulnerable source codes. The chosen vulnerability-relevant data sources are cross-domain, involving historical vulnerability data from variable software projects and data from the Software Assurance Reference Database (SARD) comprising vulnerability examples. The main purpose of this paper is to outline the state-of-the-art and to analyze and discuss open challenges with regard to the most relevant areas in the field of bio-inspired optimization based on the representation of software vulnerability. Empirical research has demonstrated that the optimized representations produced by the suggested nature-inspired optimization algorithms are feasible and efficient and can be transferred for real-world vulnerability detection.
THE RELATIONSHIP OF LEARNING HABITS WITH STUDENTS' COGNITIVE AREA LEARNING OUTCOMES IN CLASS IV PPKN SDN 03 SINGKAWANG

This study aims to: 1) describe the study habits of fourth grade students at SDN 03 Singkawang; 2) Describe the cognitive domain learning outcomes of fourth grade students at SDN 03 Singkawang; 3) determine the relationship between study habits and learning outcomes of fourth grade students at SDN 03 Singkawang. The research method is correlational or relationship with a quantitative approach. The sample of this study was students of class IV A and IV B at SDN 03 Singkawang with 24 students for class IV A. The technique used was the purposive sampling technique. The 5% (0.05) significant level data used was the normality test using the Chi square formula. The results showed; 1) study habits in the medium category as many as 24 students with a percentage of 69%; 2) Learning outcomes in the cognitive domain with a good category were 11 students with a percentage of 70%; 3) There is a relationship between study habits and learning outcomes in the cognitive domain with the criteria for the strength of the relationship in the fairly strong category with a correlation coefficient of 0.40. Learning habits provide a determinant coefficient of 16% of learning outcomes in the cognitive domain and the difference of 84% is influenced by other factors. Thus, it can be concluded that if students' learning habits are high, the higher the cognitive domain learning outcomes. On the other hand, the lower the student's study habits, the lower the student's cognitive domain learning outcomes.
TEACHING AT THE RIGHT LEVEL (TaRL) THROUGH THE ALL SMART CHILDREN APPROACH (SAC) IMPROVES STUDENT'S LITERATURE ABILITY

The TaRL approach was developed by Pratham. In his video entitled How to Teach Kids to Read in as Little as 50 Days he says: “All over the world a growing belief is that if you open a school the children will (successfully) learn. But children are not studying, for example in India 97% are enrolled in schools but 50% of children cannot read paragraphs for 2nd graders”. The stages in the implementation of SAC consist of 3 stages: assessment, grouping, and learning according to level/ability. After going through these three stages by carrying out literacy learning for 2 weeks, then a reassessment is carried out. The results of the SAC program assessment can improve children's abilities, namely at the beginner level, letters, words, and paragraphs decreased from 55% to 50%, and at the story level increased from 45% to 50%. The students who progressed to level up reached 30%. The data on increasing students' literacy skills were obtained using the Naturalistic Research method. Grade 1 to grade 6 children are assessed individually. The instrument used is a test. The tests used are letter cards, word cards, paragraph texts, and short story texts equipped with questions about the content of the story.
Classically Replaceable Operations

Quantum information science provides powerful technologies beyond the scope of classical physics. In practice, accurate control of quantum operations is a challenging task with current quantum devices. The implementation of high fidelity and multi-qubit quantum operations consumes massive resources and requires complicated hardware design to fight against noise. An approach to alleviating this problem is to replace quantum operations with classical processing. Despite the common practice of this approach, rigorous criteria to determine whether a given quantum operation is replaceable classically are still missing. In this work, we define the classically replaceable operations in four general scenarios. In each scenario, we provide their necessary and sufficient criteria and point out the corresponding classical processing. For a practically favorable case of unitary classically replaceable operations, we show that the replaced classical processing is deterministic. Beyond that, we regard the irreplaceability of quantum operations by classical processing as a quantum resource and relate it to the performance of a channel in a non-local game, as manifested in a robustness measure.
Vulnerable Students, Inclusion, and Digital Education in the Covid‐19 Pandemic: A Qualitative Case Study From Austria

Worldwide, more than 1.5 billion students had to switch to distance learning in 2020. Education took place at home, where parents attended to their children, and teachers taught their students in digital mode, while minors were physically and socially isolated from their peers. Concerning the situation in Austria, several quantitative studies expose two central challenges: First, the comprehensive lack of digital infrastructure disrupted (digital) distance learning. Second, the Covid‐19 pandemic particularly affected vulnerable students and extended educational inequality. The state of the art emphasizes a lack of qualitative studies demonstrating different perspectives on the educational situation of vulnerable students in general and with SEN in particular during the pandemic. This leads to the following research question: How do professional actors map the situation of inclusive and digital education during the Covid‐19 pandemic in Austria? To research this unprecedented situation, four focus groups with diverse stakeholders (teachers, principals, psychologists, and school board employees) discussed their experiences in the school years 2019–2020 and 2020–2021. Data were analyzed according to the Grounded Theory method of the postmodern approach referred to as “situational analysis.” The study visualizes various parallel discourses and voices within the situation of (digital distance) learning during the Covid‐19 pandemic. The theoretical context of the intersection of inclusive and digital education frames the empirical findings. Central findings relate to missing or discriminatory guidelines and policies, a lack of digital infrastructure, and altered professional‐pedagogical support that minimized or disrupted inclusive education during (digital) distance learning.
A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

Many researchers have classified acute lymphoblastic leukemia using several methods.One of the methods is a convolutional neural network.However, the limitation of the convolutional neural network is a large number of trainable parameters updated.The paper proposes a new architecture based on the convolutional neural network.We have designed and implemented a convolutional neural network with different kernels, where we increase the number of kernels like pyramid models.We utilized the final convolution to conduct the fully connected, followed by the SoftMax function to classify the image.We have evaluated our proposed architecture using acute lymphoblastic leukemia image database (ALL-IDB2).The results show that our proposed method produced the average Accuracy, Precision, and Recall of 99.17%, 99.33%, and 99%, respectively.It has outperformed other models, i.e., shape features, Multi distance of GLCM, CNN and SVM, Pretrained deep convolutional neural networks, AlexNet, ensemble network, convolutional and recurrent neural network, and hypercomplex-valued convolutional neural networks.
A Study on the Improvement of Learning Support Program Implementation for Students on Academic Probation : Focusing on the Case of T University

The purpose of this study is to discuss ways to improve the customized learning support program implementation for students on academic probation at T University by analyzing the current status of academic probation and learning support. To achieve the purpose of this study, first, T University and other universities were comparatively analyzed by the distribution of grades of major and liberal arts during the 2019-2020 academic year. Second, in order to find out the variables affecting academic probation, 1,243 students on academic probation at T University were analyzed by grade, gender, GPA, and admission type. In addition, 10 students with a high participation rate were selected and conducted FGI for the current status and cause analysis of problems of Jump Jumping, a learning support program for students on academic probation at T University. As a result, first, T University had a high ratio of those who received students under D+ grade in liberal arts, so it is required to analyze systemically about teaching and learning supports linked to Co-Curriculum so that the students can be more careful for their grade. Second, it is necessary to examine the educational program for lower grades and the minority students in consideration of the general characteristics of T University
Are emojis ready to promote the WHO 5 moments for hand hygiene in healthcare?

Hand hygiene is universally recognized as a cornerstone measure for the prevention of healthcare-associated infections. Although the WHO "My five Moments for hand hygiene" poster has been used for more than a decade to delineate hand hygiene indications and promote action, adherence levels among healthcare workers are still notoriously low and disquieting. To compensate for the lack of effective hand hygiene communication, we aimed to evaluate emojis as possible surrogates for the non-verbal aspects of hand hygiene behaviour.Following a thorough review of the Unicode version 12.0, the most applicable emojis to the terms used in the WHO 5 Moments poster were extracted. We developed a self-administered questionnaire to assess the view of infection prevention and control (IPC) practitioners regarding the use of emojis to show the WHO 5 Moments. Completed questionnaires were collected and analysed to determine the suitability of the existing emojis to illustrate a unified emoji poster. Data were analysed using R (version 3.6.3).A total of 95 IPC practitioners completed the questionnaire from May to October 2019 from different countries. Of these, 69 (74%) were female, and the mean age of the participants was 44.6 ± 10.87 years. We found appropriate emojis for six of the words used in the poster, including for touching (72%), for patient (63%), for clean (53%), for procedure (56%), for body fluid (58%), and for exposure risk (71%). The existing emojis proposed for the words "hygiene", "aseptic", and "surrounding" seemed to be less satisfactory.In summary, the findings of this study indicate that the existing emojis may not be able to substitute the words used in the WHO 5 Moments poster. Emojis might be helpful to address hand hygiene indications in healthcare that may eventually play a role in promoting this measure. However, emojis should be further studied to choose the most appropriate ones and avoid ambiguity and misinterpretation. More emojis to convey health related messages are needed. We recommend further research in this area to evaluate the effect of using emojis in healthcare-related behaviours.