Reviewerly/exHarmony huggingface dataset:
[Medical nucleus image segmentation network based on convolution and attention mechanism].

Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.深度学习在细胞核分割中具有重要作用，但在病理诊断中仍面临着细胞核图像的细微特征难以提取、核边缘模糊等问题。针对上述问题，本文提出了一种结合注意力机制的细胞核分割网络。该网络使用U型网络（UNet）作为基本结构，以深度可分离残差卷积（DSRC）模块作为特征编码，避免丢失细胞核边界信息；特征解码引入坐标注意力（CA）加强特征空间上远程距离，突出细胞核位置的关键信息；最后，设计语义信息融合（SIF）模块整合深浅层特征，改善分割效果。在2018数据科学碗（DSB2018）和三阴乳腺癌（TNBC）数据集上分别进行实验，所提方法的精确率在两个数据集上分别为92.01%、89.21%，灵敏度为90.09%、91.10%，平均交并比为89.01%、89.12%。实验结果表明，本文所提方法能有效分割细胞核细微区域，提升分割准确度，为临床诊断提供可靠依据。.
Improvement of Face Recognition Accuracy for Mask Wearers

With the spread of COVID-19, wearing masks has become mandatory in many countries around the world, and there is a need for technology to recognize facial images of people wearing masks. In this paper, we conducted experiments on learning and recognizing images of mask wearers using a method based on a pre-trained model produced by NVIDIA, and studied the recognition of face images by increasing the number of datasets using data augmentation. The experimental results show the effectiveness of the proposed method, and the proposed method is implemented on NVIDIA Jetson Xavier for real-time recognition experiments.
Energy Consumption Analysis of Pruned Semantic Segmentation Networks on an Embedded GPU

Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitations in terms of energy consumption prohibit the use of very large networks, that typically reach the best performance. A common method to reduce the complexity of these architectures, without sacrificing accuracy, is to rely on pruning, in which the least important portions are eliminated. There is a large literature on the subject, but interestingly few works have measured the actual impact of pruning on energy. In this work, we are interested in measuring it in the specific context of semantic segmentation for autonomous driving, using the Cityscapes dataset. To this end, we analyze the impact of recently proposed structured pruning methods when trained architectures are deployed on a Jetson Xavier embedded GPU.
Performance Evaluation on Collaborative Filtering using Latent Topics under Several Sparsity Levels

A recommendation system is one of the methods to support users' information acquisition. Collaborative filtering (CF) is a popular method to achieve it. Users' preferences (how much users like items) are represented as rating matrix for using CF. However, the percentage of missing ratings (called sparsity) is usually high, which results in poor performance. To address this problem, a hybrid method has been proposed to mitigate the performance degradation even at high sparsity by combining not only the ratings but also other information about items and users. Collaborative topic regression (CTR) is a pioneer model, which extracts latent topics from documents about items or users and uses them together with rating matrix. However, due to the increase of model complexity of CTR compared to CF, the expected performance will not be achieved unless the word selection in the document and the hyperparameter settings should be done appropriately. In this study, we experimentally show how the performance of CTR changes under different sparsity levels, by varying the words selected based on document frequency and by varying the combination of hyperparameters that adjust the influence of the ratings and that of the latent topics. We prepare three datasets with different domains and evaluate the generality of the results in our experiments.
Exploring Relevance and Coherence for Automated Text Scoring using Multi-task Learning

With the explosive growth of the information on the Internet, the evaluation of the quality and credibility of web content has become more important than ever before.In this work, we focus on the quality assessment of texts.Recently, various methods have been proposed for the automated text scoring task and obtained competitive results.However, few studies have focused on both relevance and coherence, which are two important factors in evaluating text quality.To improve the scoring task, we propose two auxiliary tasks using negative sampling and integrate them into a multi-task learning framework.The first auxiliary task is relevance modeling and the other one is coherence modeling.We evaluate our model on the Automated Student Assessment Prize (ASAP) dataset.Experimental results show that our model achieves higher Quadratic Weighted Kappa (QWK) scores with an improvement of 1.5% on average.
Music Cataloging and Technological Change in the 1980s: A Personal History

After decades of relative stasis, library technology underwent transformational changes in the 1980s. Over the course of a few years, the introduction of shared online cataloging, the local online public catalog, and electronic mail changed the way librarians did their work and ushered in a period of technological innovation that continues today. The author offers a first-hand account of the impact of these technologies in the 1980s, when he was a graduate student and early-career music librarian.
Ontology-Based Linked Data to Support Decision-Making within Universities

In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different purposes in the academic domain. Finally, we compared the results of this link with previous work, as evidence of the accuracy of leveraging this technology to improve decisions within universities.
Don't CWEAT It

To help prevent hardware security vulnerabilities from propagating to later design stages where fixes are costly, it is crucial to identify security concerns as early as possible, such as in RTL designs. In this work, we investigate the practical implications and feasibility of producing a set of security-specific scanners that operate on Verilog source files. The scanners indicate parts of code that might contain one of a set of MITRE's common weakness enumerations (CWEs). We explore the CWE database to characterize the scope and attributes of the CWEs and identify those that are amenable to static analysis. We prototype scanners and evaluate them on 11 open source designs - 4 system-on-chips (SoC) and 7 processor cores - and explore the nature of identified weaknesses. Our analysis reported 53 potential weaknesses in the OpenPiton SoC used in [email protected], 11 of which we confirmed as security concerns.
Strategies on Instructional Delivery Method and Pupils’ Proficiency of the Pull-out Service Model in Elementary Special Education Setting

The study aimed to develop strategies on instructional delivery method and pupilsâ proficiency of the pull-out service model in special education setting in the three Maryland elementary schools of the research district in the eastern state of USA by determining the extent of utilization of instructional delivery method in terms of information processing, behavioral, social interaction, and personal, the level of proficiency of pupils in terms of Mathematics and Reading, and the issues and concerns in the pull-out service model in elementary special education setting. The subjects of this study were 65 teachers of the three Maryland elementary schools which were selected using purposive sampling. The study utilized descriptive and inferential statistics after data collection and tabulation. Weighted mean was utilized to describe the instructional delivery method and pupilsâ proficiency of the pull-out service model in elementary special education setting. F-test was employed to determine the significant difference in the assessment of the utilization of instructional delivery method and pupilsâ proficient indicators. T-test was employed to determine the significant difference between the pupilsâ proficiency in Mathematics and Reading. Pearsonâs determined if the instructional delivery method related to the proficiency of pupils. Results revealed that social interaction is not widely utilized. Meanwhile, the pupils have low level of proficiency in reading followed by mathematics. Further, the major issue in the pull-out program is the self-esteem of pupils with disability.
AN EXPLORATION OF BARRIERS IN LEARNING TO SPEAK ENGLISH IN ONLINE SETTING AMIDST COVID-19 PANDEMIC: A CASE OF SOPHOMORE STUDENTS IN AN INDONESIAN ISLAMIC UNIVERSITY

Abstract Despite the global recognition of online learning as the best available option to keep enacting educational practices, particularly foreign language learning, amidst the Covid-19 pandemic, many concern about the constraints faced by students in this unpredicted distance learning. Involving second-year students in an Islamic state university in the province of Lampung, this study aimed to investigate EFL leaners’ barriers in learning speaking in online classroom and how they cope with the barriers. The data were generated through open-ended questionnaires and then analyzed using Miles and Huberman Model. The findings of the study classified three types of barriers, namely technical barriers such as bad internet connection and running out of data quota, linguistic barriers such as by lack of vocabularies, grammar and pronunciation, and personal emotional barriers such as lack of confidence and afraid of making mistakes. Meanwhile, the coping strategies with these obstacles were using more providers and finding area with good signal for technical strategies. For linguistics Strategies, learners listened the conversation and the song, memorized more vocabularies, comprehended more grammar, Reading a lot and highlight the unknown words, found the meaning of unknown words from dictionary, Watched more videos and films, discussed everything in English with more friends, practice to speak in front of the mirror and recorded it. While in Personal Emotional, learners increased their confidence by forcing and convincing themselves to speak and always keep trying, everything they felt was the challenge, ignored of being afraid on making mistakes, not be panic and take a deep breath to make them relax and enjoy every situation. Keywords: English speaking, barriers, online learning, Covid-19
Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personas

User-centric design within organizations is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations are to adopt persona thinking. To address these concerns, we develop and validate the Persona Readiness Scale (PRS), a survey instrument to measure organizational readiness for personas. After a 12-person qualitative pilot study, the PRS was administered to 372 professionals across different industries to examine its reliability and validity, including 125 for exploratory factor analysis and 247 for confirmatory factor analysis. The confirmatory factor analysis indicated a good fit with five dimensions: Culture readiness, Knowledge readiness, Data and systems readiness, Capability readiness, and Goal readiness. Higher persona readiness is positively associated with the respondents' evaluations of successful persona projects. Organizations can apply the resulting 18-item scale to identify areas of improvement before initiating costly persona projects towards the overarching goal of user-centric product development. Located at the cross-section of information systems and human-computer interaction, our research provides a valuable instrument for organizations wanting to leverage personas towards more user-centric and empathetic decision making about users.The online version contains supplementary material available at 10.1007/s10799-022-00373-9.
A customer satisfaction centric food delivery system based on blockchain and smart contract

Food delivery systems are gaining popularity recently due to the expansion of internet connectivity and for the increasing availability of devices. The growing popularity of such systems has raised concerns regarding (i) Information security, (ii) Business to business (B2B) deep discounting race, and (iii) Strict policy enforcement. Sensitive personal data and financial information of the users must be safeguarded. Additionally, in pursuit of gaining profit, the restaurants tend to offer deep discounts resulting in a higher volume of orders than usual. Therefore, the restaurants and the delivery persons fail to maintain the delivery time and often impair the food quality. In this paper, we have proposed a blockchain and smart contract-based food delivery system to address these issues. The main goal is to remove commission schemes and decrease service delays caused by a high volume of orders. The protocols have been deployed and tested on the Ethereum test network. The simulation manifests a successful implementation of our desired system; with the payment being controlled by our system. The actors (restaurant, delivery-person or consumer) are bound to be compliant with the policies or penalized otherwise.
Learning Rule Parameters of Possibilistic Rule-Based System

In this paper, we introduce a learning paradigm of the rule parameters of a possibilistic rule-based system, given training data. For a rule-based system composed of n if-then parallel possibilistic rules, we introduce an equation system denoted (Σ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ), which is analogous to the Farreny-Prade equation system. The unknown part of the system (Σ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ) is a vector composed of the rule parameters, whose values must be determined according to training data.We establish necessary and sufficient conditions for the system (Σ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ) to be consistent. If this is the case, we show that the set of solutions of the system is a Cartesian product of subintervals of [0, 1] whose bounds are computed. Then, we deduce that there are a unique maximal solution and, as it is well known by Sanchez’s work on the solving of min-max fuzzy relational equations, a unique minimal one. These results are proved by relating the solutions of (Σ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ) to those of the equation system given by the first n − 1 possibilistic rules equipped with a second member which is constructed from that of (Σ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ).Finally, our results are illustrated by an example.
A study into patient similarity through representation learning from medical records

Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines.
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation

Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achieved promising cross-modality medical image segmentation by transferring knowledge from a label-rich source domain to an unlabeled target domain. However, it is also difficult to collect annotations from the source domain in many clinical applications, rendering most prior works suboptimal with the label-scarce source domain, particularly for few-shot scenarios, where only a few source labels are accessible. To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance. In our framework, hallucination and segmentation models are jointly trained with the gradient-based meta-learning strategy to synthesize examples that lead to good segmentation performance on the target domain. To further facilitate data hallucination and cross-domain knowledge transfer, we develop a self-ensembling model with a hallucination-consistent property. Our meta-hallucinator can seamlessly collaborate with the meta-segmenter for learning to hallucinate with mutual benefits from a combined view of meta-learning and self-ensembling learning. Extensive studies on MM-WHS 2017 dataset for cross-modality cardiac segmentation demonstrate that our method performs favorably against various approaches by a lot in the few-shot UDA scenario.
Digital Literacy of Pre-service Teachers in the Period Time of COVID-19 Pandemic

Digital literacy is an important thing for modern pre-service teachers, they are key element to deal with technology for education and motivate classroom with effective infortion technology. This study aims to explore digital literacy of pre-service teachers during time of COVID-19 pandemic. The respondents were 54 pre-service teachers, rated the digital literacy based on the framework of teacher digital literacy, which consisted of 5 components: (1) social responsibility, (2) team-based learning, (3) information management, (4) processing and information presentation, and (5) digital integrity. Result revealed that the level of digital literacy of pre-service teachers is at high level in all components. It indicated that they are ready to deal with technology for education, but digital literacy is still engaged into them and in-service science teachers.
A Formalization of the Change of Variables Formula for Integrals in mathlib

We report on a formalization of the change of variables formula in integrals, in the mathlib library for Lean. Our version of this theorem is extremely general, and builds on developments in linear algebra, analysis, measure theory and descriptive set theory. The interplay between these domains is transparent thanks to the highly integrated development model of mathlib.
Stylistic Fingerprints, POS-tags, and Inflected Languages: A Case Study in Polish

The study discusses modeling diachronic processes by logistic regression. The phenomenon of nonlinear changes in language was first observed by Raimund Piotrowski (hence labelled as Piotrowski's law), even if actual linguistic evidence usually speaks against using the notion of a "law" in this context. In our study, we apply logistic regression models to 9 changes which occurred between 15th and 18th century in the Polish language. The attested course of the majority of these changes closely follow the expected values, which proves that the language change might indeed resemble a nonlinear phase change scenario. We also extend the original Piotrowski's approach by proposing polynomial logistic regression for these cases which can hardly be described by its standard version. Also, we propose to consider individual language change cases jointly, in order to inspect their possible collinearity or, more likely, their different dynamics in the function of time. Last but not least, we evaluate our results by testing the influence of the subcorpus size on the model's goodness-of-fit.