Reviewerly/exHarmony huggingface dataset:
Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation

Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human–machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimation. Yet rapid and irregular motion patterns, varying image contrast and domain shifts in imaging devices pose a severe challenge to conventional realtime registration approaches. While learning-based registration networks have the promise of abstracting relevant features and delivering very fast inference times, they come at the potential risk of limited generalisation and robustness for unseen data; in particular, when trained with limited supervision. In this work, we demonstrate that these issues can be overcome by using end-to-end differentiable displacement optimisation. Our method involves a trainable feature backbone, a correlation layer that evaluates a large range of displacement options simultaneously and a differentiable regularisation module that ensures smooth and plausible deformation. In extensive experiments on public and private ultrasound datasets with very sparse ground truth annotation the method showed better generalisation abilities and overall accuracy than a VoxelMorph network with the same feature backbone, while being two times faster at inference.
Prediction of College Students' Classroom Learning Effect Considering Positive Learning Emotion

Exploring the influence of positive learning emotion on college students' classroom learning effect facilitates fully understanding college students' online learning effect and emotional state, and is beneficial to improving students' learning quality and teachers' teaching quality. At present, few scholars have summative evaluation of students' classroom learning effect from the perspective of students' learning emotions and prove from the perspective of theory and practice that good emotional state is an important influencing factor to improve college students' classroom learning effect. Therefore, this article fully considers the positive learning emotion, and makes a research on the prediction of college students' classroom learning effect. Firstly, this article defines the behavior data of students in the online learning process based on learning emotions, and studies the correlation between college students' classroom learning behaviors based on Hawkes process. Then, based on the learning participation under the influence of different learning emotions, the online learning effect of students is quantified, and the prediction model of students' classroom learning is constructed by combining the learning behavior sequence analysis results represented by Hawkes process and the characteristic information of students themselves and courses. The experimental results verify the effectiveness of the model, and the significance test results confirm the positive effect of positive emotion on learning effect.
Using the Homosaurus in a Public Library Consortium: A Case Study

Library of Congress Subject Headings (LCSH) on LGBTQIA+ topics have not evolved alongside the frequently changing vernacular vocabulary used to access resources on the topics. To rectify this issue, libraries can choose to use an alternative controlled vocabulary, like The Homosaurus: An International LGBTQ+ Linked Data Vocabulary. This case study provides an overview of how Cooperative Computer Services (CCS), a public library consortium in Illinois, made the case to allow the Homosaurus in the CCS catalog, gained approval from the member libraries, and crafted a cataloging manual section. Other libraries can follow the recommendations in this article on how to properly make the case for the approval of a policy to allow an alternative controlled vocabulary in their catalog.
Qualitative Analysis of Anomaly Detection in Time Series

We present an end-end system for time series anomaly detection specifically aimed at detecting fraudulent transactions in bank transaction datasets. The goal of anomaly detection is to locate unusual or uncommon occurrences in data. Outlier detection, one of the most crucial data analysis jobs, has many practical uses. For instance, in order to track their company data and send out notifications for outliers, Yahoo and Microsoft have developed their own services for time-series outlier identification. However, majority of outlier identification methods employ classifications of unusual entries without providing any justification. Therefore, the goal is to make the anomalous points as predicted by the model more interpretable and thereby create a flagging system to help human labelers in determining anomalous behavior. This will help reduce human engineering efforts by a huge margin.
The Effect of E-Commerce and Purchase Effectiveness on Student Learning

E-commerce is the practice of purchasing and selling products or services online or through other electronic channels. E-commerce is expanding quite quickly right now, as seen by the rise in e-commerce sites like Tokopedia, Shopee, and Lazada. The study examined how e-commerce affects students' learning, who frequently utilize it as a tool to fill their learning needs. With the rise in student e-commerce, learning is a must for students if they want to enhance knowledge generally. Analyzing whether e-commerce has a positive or negative impact on enhancing learning is required. The results showed that only the primary channel variable for e-commerce transactions (X1) had a significant influence on the student learning variable (Y), whereas the ease-of-use variable (X2), learning correlation (X3), and transaction variable (X4) did not have a significant effect on student learning. The tests had been conducted using the SPSS software to test the classical assumptions. Some e-commerce specialists believed that e-commerce was only utilized to address fundamental necessities and lifestyle requirements, not educational requirements.
Designing a Visual Cryptography Curriculum for K-12 Education

We have designed and developed a simple, visual, and narrative K-12 cybersecurity curriculum leveraging the Scratch programming platform to demonstrate and teach fundamental cybersecurity concepts such as confidentiality, integrity protection, and authentication. The visual curriculum simulates a real-world scenario of a user and a bank performing a banking transaction and an adversary attempting to attack the transaction. The curriculum first introduces students to three visual scenarios demonstrating attacks that exist when systems do not integrate the fundamental concepts of confidentiality, integrity protection, and authentication. Then, it introduces them to three visual scenarios that build on the attacks to demonstrate and teach how the fundamental concepts can be used to defend against them.
Simulating Cybersecurity Risk Using Advanced Quantitative Risk Assessment Techniques

This paper; a scenario-based teaching case study, aims to introduce students in a Cybersecurity Risk Management course to advanced quantitative risk assessment techniques. The case study utilizes a fictitious company for which a risk assessment is underway. Assuming the role of the Cybersecurity Risk Team of the company, students are tasked with determining the risk exposure the company faces from a threat scenario against one of its mission-critical information resources. Specifically, the students are required to (1) quantify the monetary losses that could result from a threat scenario, (2) compute the inherited risk exposure from the threat scenario, (3) compute the residual risk given the implantation of certain security controls, and (4) compute the rate of return on the security controls. The case study holds the promise of enhancing the overall learning of the students and boosting their marketability as future cybersecurity professionals.
Challenging machine translation engines: some Spanish-English linguistic problems put to the test

This work is an evaluation of machine translation engines completed in 2018 and 2021, inspired by Isabelle, Cherry &amp; Foster (2017), and Isabelle &amp; Kuhn (2018). The challenge consisted of testing MTs Google Translate and Bing and DeepL in the translation of certain linguistic problems normally found when translating from Spanish into English. The divergences representing a “challenge” to the engines were of morphological and lexical-syntactical types. The absolute winner of the challenge was DeepL, in second place was Bing from Microsoft, and Google was the engine that was the poorest in the management of the linguistic problems. In terms of time, when comparing the engines three years apart, it was found that DeepL was the only one that enhanced its performance by correcting a problem it had before in a test sentence. This was not the case for the other two, on the contrary, their translations were of lower quality. These machines do not seem to be consistent in the manner in which they are improved. These findings may be valuable for translators who may work with these systems as pre or post-editors so that their efforts may be better directed.
Simulating quantum error mitigation in fermionic encodings

The most scalable proposed methods of simulating lattice fermions on noisy quantum computers employ encodings that eliminate nonlocal operators using a constant factor more qubits and a nontrivial stabilizer group. In this work, we investigated the most straightforward error mitigation strategy using the stabilizer group, stabilizer postselection, that is very natural to the setting of fermionic quantum simulation. We numerically investigate the performance of the error mitigation strategy on a range of systems containing up to 42 qubits and on a number of fundamental quantum simulation tasks including non-equilibrium dynamics and variational ground state calculations. We find that at reasonable noise rates and system sizes, the fidelity of computations can be increased significantly beyond what can be achieved with the standard Jordan-Wigner transformation at the cost of increasing the number of shots by less than a factor of 10, potentially providing a meaningful boost to near-term quantum simulations. Our simulations are enabled by new classical simulation algorithms that scale with the logical Hilbert space dimension rather than the physical Hilbert space dimension.
A Practical Guide for Generating Reproducible and Programmatic Neuroimaging Visualizations

Neuroimaging visualizations form the centerpiece of the interpretation and communication of scientific results, and are also a cornerstone for data quality control. Often, these images and figures are produced by manually changing settings on Graphical User Interfaces (GUIs). There now exist many well-documented code-based brain visualization tools that allow users to programmatically generate publication-ready figures directly within R, Python and MATLAB environments. Here, we provide a rationale for the wide-spread adoption of code-generated brain visualizations by highlighting corresponding advantages in replicability, flexibility, and integration over GUI based tools. We then provide a practical guide outlining the steps required to generate these code-based brain visualizations. We also present a comprehensive table of tools currently available for programmatic brain visualizations (https://sidchop.shinyapps.io/braincode_selector/) and provide didactic examples of visualizations and associated code as a point of reference. Finally, we provide a web-app (https://sidchop.shinyapps.io/braincode/) which can generate simple code-templates as starting points for these visualizations.
Pemanfaatan Aplikasi Let's Read dalam Meningkatkan Literasi Membaca Siswa Kelas 2 Sekolah Dasar

This research was conducted in grade 2 of elementary school and was motivated by a lack of reading literacy among elementary school students. The purpose of this study was to determine the effect of using the Let's Read application on reading literacy of 2nd grade elementary school students. This study used quantitative research which in this study required 2 classes, namely class 2A and class 2B with a description of class 2A as the experimental class and class 2B as the control class. The reading literacy questionnaire is the instrument used in this study. Data processing shows that the use of the Let's Read application has a significant impact on reading literacy among second grade students.
End-to-End Speech Recognition: A Survey

In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning. In the wake of this transition, a number of all-neural ASR architectures were introduced. These so-called end-to-end (E2E) models provide highly integrated, completely neural ASR models, which rely strongly on general machine learning knowledge, learn more consistently from data, while depending less on ASR domain-specific experience. The success and enthusiastic adoption of deep learning accompanied by more generic model architectures lead to E2E models now becoming the prominent ASR approach. The goal of this survey is to provide a taxonomy of E2E ASR models and corresponding improvements, and to discuss their properties and their relation to the classical hidden Markov model (HMM) based ASR architecture. All relevant aspects of E2E ASR are covered in this work: modeling, training, decoding, and external language model integration, accompanied by discussions of performance and deployment opportunities, as well as an outlook into potential future developments.
Efficient Noise Mitigation Technique for Quantum Computing

Quantum computers have enabled solving problems beyond the current computers' capabilities. However, this requires handling noise arising from unwanted interactions in these systems. Several protocols have been proposed to address efficient and accurate quantum noise profiling and mitigation. In this work, we propose a novel protocol that efficiently estimates the average output of a noisy quantum device to be used for quantum noise mitigation. The multi-qubit system average behavior is approximated as a special form of a Pauli Channel where Clifford gates are used to estimate the average output for circuits of different depths. The characterized Pauli channel error rates, and state preparation and measurement errors are then used to construct the outputs for different depths thereby eliminating the need for large simulations and enabling efficient mitigation. We demonstrate the efficiency of the proposed protocol on four IBM Q 5-qubit quantum devices. Our method demonstrates improved accuracy with efficient noise characterization. We report up to 88\% and 69\% improvement for the proposed approach compared to the unmitigated, and pure measurement error mitigation approaches, respectively.
Enhancement of CNN-based 2048 Player with Monte-Carlo Tree Search

In this study, we developed computer players for a single-player stochastic game 2048 using an existing neural-network evaluation function and a version of Monte-Carlo tree search. We applied the Monte-Carlo softmax search (MCSS) algorithm, with some modifications in order to adapt it to the stochastic game, and designed six methods of controlling the search algorithm. We evaluated the MCSS players in an exhaustive manner and also conducted longer experiments for two MCSS players by changing the number of simulations per move. Our MCSS player achieved an average score of 533 542 under the limit of 2000 simulations per move. This result was better than Expectimax players that used the same evaluation function.
The Role of Text To Speech Assistive Technology to Improve Reading Ability in E-Learning for ADHD Students

This study aims to determine the role of text to speech assistive technology on the reading ability of ADHD students in learning using e-learning. The ability of ADHD students who have impulsive and hyperactive behavior shows that students who are diagnosed will have difficulty focusing on one thing as a result of coordination functions and emotional responses that affect learning abilities, especially in reading. To improve reading skills, the use of assistive text to speech technology is needed by students during e-learning. The purpose of this study was to determine the role of text to speech assistive technology to improve the reading ability of ADHD students in reading spelling of words, with a clear voice and straightforward word emphasis. The method used in this study is Single Subject Research (SSR) using the ABA technique. Instruments to determine the level of reading ability of ADHD students are observation and documentation. The results showed that the role of text to speech assistive technology to improve reading skills in e-learning for ADHD students made progress in spelling words and with clear vowels and straightforward word emphasis. It can be concluded that the role of text to speech assistive technology to improve reading skills in e-learning for ADHD students is highly recommended to be applied in every e-learning lesson. It can be concluded that the role of text to speech assistive technology to improve reading skills in e-learning for ADHD students is highly recommended to be applied in every e-learning lesson.
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization

We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization. Photo-realistic images of procedurally generated candies are rendered in a controlled environment under multiple lightning conditions, also providing depth and normal maps in an industrial conveyor scenario. We make available anomaly-free samples for model training and validation, while anomalous instances with precise ground-truth annotations are provided only in the test set. The dataset comprises ten classes of candies, each showing different challenges, such as complex textures, self-occlusions and specularities. Furthermore, we achieve large intra-class variation by randomly drawing key parameters of a procedural rendering pipeline, which enables the creation of an arbitrary number of instances with photo-realistic appearance. Likewise, anomalies are injected into the rendering graph and pixel-wise annotations are automatically generated, overcoming human-biases and possible inconsistencies. We believe this dataset may encourage the exploration of original approaches to solve the anomaly detection task, e.g. by combining color, depth and normal maps, as they are not provided by most of the existing datasets. Indeed, in order to demonstrate how exploiting additional information may actually lead to higher detection performance, we show the results obtained by training a deep convolutional autoencoder to reconstruct different combinations of inputs.
Malaria detection using Deep Convolution Neural Network

The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlying reason is international funding has declined. Malaria, which is spread to people through the bites of infected female mosquitoes, occurs in 91 countries but about 90% of the cases and deaths are in sub-Saharan Africa. The disease killed 4,35,000 people last year, the majority of them children under five in Africa. AI-backed technology has revolutionized malaria detection in some regions of Africa and the future impact of such work can be revolutionary. The malaria Cell Image Data-set is taken from the official NIH Website NIH data. The aim of the collection of the dataset was to reduce the burden for microscopists in resource-constrained regions and improve diagnostic accuracy using an AI-based algorithm to detect and segment the red blood cells. The goal of this work is to show that the state of the art accuracy can be obtained even by using 2 layer convolution network and show a new baseline in Malaria detection efforts using AI.
Neural Compositional Rule Learning for Knowledge Graph Reasoning

Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well as their ability to generalize to other tasks, domains, and data. While recent methods have been proposed to learn logical rules, the majority of these methods are either restricted by their computational complexity and can not handle the large search space of large-scale KGs, or show poor generalization when exposed to data outside the training set. In this paper, we propose an end-to-end neural model for learning compositional logical rules called NCRL. NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head. By recurrently merging compositions in the rule body with a recurrent attention unit, NCRL finally predicts a single rule head. Experimental results show that NCRL learns high-quality rules, as well as being generalizable. Specifically, we show that NCRL is scalable, efficient, and yields state-of-the-art results for knowledge graph completion on large-scale KGs. Moreover, we test NCRL for systematic generalization by learning to reason on small-scale observed graphs and evaluating on larger unseen ones.
A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization

Long-form clinical summarization of hospital admissions has real-world significance because of its potential to help both clinicians and patients. The faithfulness of summaries is critical to their safe usage in clinical settings. To better understand the limitations of abstractive systems, as well as the suitability of existing evaluation metrics, we benchmark faithfulness metrics against fine-grained human annotations for model-generated summaries of a patient's Brief Hospital Course. We create a corpus of patient hospital admissions and summaries for a cohort of HIV patients, each with complex medical histories. Annotators are presented with summaries and source notes, and asked to categorize manually highlighted summary elements (clinical entities like conditions and medications as well as actions like "following up") into one of three categories: ``Incorrect,'' ``Missing,'' and ``Not in Notes.'' We meta-evaluate a broad set of proposed faithfulness metrics and, across metrics, explore the importance of domain adaptation (e.g. the impact of in-domain pre-training and metric fine-tuning), the use of source-summary alignments, and the effects of distilling a single metric from an ensemble of pre-existing metrics. Off-the-shelf metrics with no exposure to clinical text correlate well yet overly rely on summary extractiveness. As a practical guide to long-form clinical narrative summarization, we find that most metrics correlate best to human judgments when provided with one summary sentence at a time and a minimal set of relevant source context.