Reviewerly/exHarmony huggingface dataset:
Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence

Abstract Objectives This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model. Methods To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions. Using eye-tracking technology, we investigated medical doctors' reading workflow during manual chart review. Moreover, we performed a clinical use study where medical doctors read two admissions with and without AI assistance to evaluate performance when using and perception of using the AI model. Results The AI model had a sensitivity of 93.7% and a specificity of 98.1% on the test cohort. In the use studies, we found that medical doctors missed more than 33% of relevant sentences when doing chart review without AI assistance. Hemorrhage events described in paragraphs were more often overlooked compared with bullet-pointed hemorrhage mentions. With AI-assisted chart review, medical doctors identified 48 and 49 percentage points more hemorrhage events than without assistance in two admissions, and they were generally positive toward using the AI model as a supporting tool. Conclusion Medical doctors identified more hemorrhage events with AI-assisted chart review and they were generally positive toward using the AI model.
Unsupervised Vehicle Re-Identification Based on Cross-Style Semi-Supervised Pre-Training and Feature Cross-Division

Vehicle Re-Identification (Re-ID) based on Unsupervised Domain Adaptation (UDA) has shown promising performance. However, two main issues still exist: (1) existing methods that use Generative Adversarial Networks (GANs) for domain gap alleviation combine supervised learning with hard labels of the source domain, resulting in a mismatch between style transfer data and hard labels; (2) pseudo label assignment in the fine-tuning stage is solely determined by similarity measures of global features using clustering algorithms, leading to inevitable label noise in generated pseudo labels. To tackle these issues, this paper proposes an unsupervised vehicle re-identification framework based on cross-style semi-supervised pre-training and feature cross-division. The framework consists of two parts: cross-style semi-supervised pre-training (CSP) and feature cross-division (FCD) for model fine-tuning. The CSP module generates style transfer data containing source domain content and target domain style using a style transfer network, and then pre-trains the model in a semi-supervised manner using both source domain and style transfer data. A pseudo-label reassignment strategy is designed to generate soft labels assigned to the style transfer data. The FCD module obtains feature partitions through a novel interactive division to reduce the dependence of pseudo-labels on global features, and the final similarity measurement combines the results of partition features and global features. Experimental results on the VehicleID and VeRi-776 datasets show that the proposed method outperforms existing unsupervised vehicle re-identification methods. Compared with the last best method on each dataset, the method proposed in this paper improves the mAP by 0.63% and the Rank-1 by 0.73% on the three sub-datasets of VehicleID on average, and it improves mAP by 0.9% and Rank-1 by 1% on VeRi-776 dataset.
Artificial intelligence can accurately distinguish IgA nephropathy from diabetic nephropathy under Masson staining and becomes an important assistant for renal pathologists

Introduction Hyperplasia of the mesangial area is common in IgA nephropathy (IgAN) and diabetic nephropathy (DN), and it is often difficult to distinguish them by light microscopy alone, especially in the absence of clinical data. At present, artificial intelligence (AI) is widely used in pathological diagnosis, but mainly in tumor pathology. The application of AI in renal pathological is still in its infancy. Methods Patients diagnosed as IgAN or DN by renal biopsy in First Affiliated Hospital of Zhejiang Chinese Medicine University from September 1, 2020 to April 30, 2022 were selected as the training set, and patients who diagnosed from May 1, 2022 to June 30, 2022 were selected as the test set. We focused on the glomerulus and captured the field of the glomerulus in Masson staining WSI at 200x magnification, all in 1,000 × 1,000 pixels JPEG format. We augmented the data from training set through minor affine transformation, and then randomly split the training set into training and adjustment data according to 8:2. The training data and the Yolov5 6.1 algorithm were used to train the AI model with constant adjustment of parameters according to the adjusted data. Finally, we obtained the optimal model, tested this model with test set and compared it with renal pathologists. Results AI can accurately detect the glomeruli. The overall accuracy of AI glomerulus detection was 98.67% and the omission rate was only 1.30%. No Intact glomerulus was missed. The overall accuracy of AI reached 73.24%, among which the accuracy of IgAN reached 77.27% and DN reached 69.59%. The AUC of IgAN was 0.733 and that of DN was 0.627. In addition, compared with renal pathologists, AI can distinguish IgAN from DN more quickly and accurately, and has higher consistency. Discussion We constructed an AI model based on Masson staining images of renal tissue to distinguish IgAN from DN. This model has also been successfully deployed in the work of renal pathologists to assist them in their daily diagnosis and teaching work.
Any Time At All: tweet frequency around the clock

Please consider this a “supplementary analysis” to my previous post looking at the frequency of tweets from my personal account over the last 12 years. I was curious about what times I was active on Twitter (measured by when I tweeted). Others might be interested in a solution to look at this in R. The code As in the previous post, we need to get the data into R and then make sure we have a date object to work with. The data comes from the tweets.js file that comes as part...
Free Bird: moving to Mastodon

There’s plenty of guides to getting going on Mastodon, aimed at people leaving Twitter. I just wanted to post a couple of technical points about making the switch that might be of interest to people who maintain webpages with Twitter content (feeds, embeds). Mastodon status updates (feed/timeline) Twitter provided a widget that meant that an account’s timeline could be embedded on a website. Migration means replacing it with a mastodon equivalent. I found this project by...
The Package: learning how to build an R package

I recently made my first R package and was asked how I did it. The answer of course was: I searched, read, and stumbled around until it was done. But having gone through the process I figured it was worthwhile summarising what I did and what I found tricky. First off, there are a ton of resources out there that describe how to go about building a package. Here are a few that I found useful: Hilary Parker’s short guide –...
Pledging My Time V: analysing race results in R

It’s been a while since I posted a breakdown of half marathon times. The last time seems to have been 2018. I decided to give my old code a clean-up and quickly crunched the numbers from the 2022 Kenilworth Half Marathon. First, the results: Briefly, the code below reads in a csv file of race results downloaded from the provider. A little bit of wrangling is required to make them plotting-friendly and then six ggplots can be made to look at pace, time and...
I’ve Gotta Get A Message To You II: fixing SSMTP gmail authorisation

This is a ultra-short post related to sending email via the command line on Linux/Rasperry Pi. On 30 May 2022, Google disabled the handy feature that allowed less secure sign in to Google accounts. This feature was a handy way to authenticate command-line routines that send email. I noticed my daily emails had stopped, and when I tested sending an email using ssmtp: echo "Hello, World!" | ssmtp fakeaddress@someplace.com sure enough: ssmtp: Authorization failed (535 5.7.8 ...
Stability of PPT in equilibrium states

We use simple spectral perturbation theory to show that the positive partial transpose property is stable under bounded perturbations of the Hamiltonian, for equilibrium states in infinite dimensions. The result holds provided the temperature is high enough, or equivalently, provided the perturbation is small enough.
Nonlinear improvement of measurement-device-independent quantum key distribution using multimode quantum memory

This paper proposes a quantum key distribution (QKD) scheme for measurement-device-independent QKD (MDI-QKD) utilizing quantum memory (QM), which is based on two distinct functions of QM: on-demand storage and multimode storage. We demonstrate a nonlinear increase in the secure key rate due to the utilization of QM. In the protocol incorporating on-demand storage, it is acknowledged that the secure key rate is scaled by $$R=O(\sqrt{\eta _{{\text {ch}}}})$$ as $$\eta _{{\text {ch}}}$$ , while as an alternative approach, we reveal that the improvement is $$O(m_{\text {s}}^2)$$ , with $$m_{\text {s}}$$ being the number of modes in frequency (spatial) multiplexing in the scheme incorporating multimode storage. We adopt an atomic frequency comb as a QM that incorporates the two functions and propose an architecture based on MDI-QKD to attain experimental feasibility. This scheme can be extended to quantum repeaters, and even for a single quantum-repeater node, there is a nonlinear enhancement and an experimental incentive to increase the number of modes.
Exploring Hyperparameter Usage and Tuning in Machine Learning Research

The success of machine learning (ML) models depends on careful experimentation and optimization of their hyperparameters. Tuning can affect the reliability and accuracy of a trained model and is the subject of ongoing research. However, little is known on whether and how hyperparameters are used and optimized in research practice. This lack of knowledge not only limits the adoption of best practices for tuning in research, but also affects the reproducibility of published results. Our research systematically analyzes the use and tuning of hyperparameters in ML publications. For this, we analyze 2000 code repositories and their associated research papers from Papers with Code. We compare the use and tuning of hyperparameters of three widely used ML libraries: scikit-learn, TensorFlow, and PyTorch. Our results show that the most of the available hyperparameters remain untouched, and those that have been changed use constant values. In particular, there is a significant difference between tuning hyperparameters and the reporting about it in the corresponding research papers. Our results suggest that there is a need for improved research and reporting practices when using ML methods to improve the reproducibility of published results.
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality

Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various heuristics. However, whether these heuristics can apply to non-grid-based problem settings while maintaining their effectiveness remains an open question. In this work, we find that the answer is prone to be no. To this end, we propose a learning-based component, i.e., the Graph Transformer, as a heuristic function to accelerate the planning. The proposed method is provably complete and bounded-suboptimal with any desired factor. We conduct extensive experiments on two environments with dense graphs. Results show that the proposed Graph Transformer can be trained in problem instances with relatively few agents and generalizes well to a larger number of agents, while achieving better performance than state-of-the-art methods.
Penerapan Metode Guru Kunjung Untuk Meningkatkan Hasil Belajar Siswa Pada Mata Pelajaran Matematika Materi Perbandingan Di Kelas IV SDN 004 Batu Ampar Kota Batam

The purpose of this study is to improve students' learning outcomes in comparative material math subjects with the teacher's methods. This study consists of two cycles, in order to break the student's learning outcomes from pre-bullying conditions, cycles I and II also to find out the supporting factors and inhibition of student learning outcomes in comparative material mathematics subjects. The results showed that pre-cycle learning results were 46,6 %, cycle to I results averaged 73,2% and cycle II results were 86,6%. Thus it can be asked successfully and reach the indicator determined by the researcher, which is 80% completion. Based on the results of the study it can be concluded that the application of teacher methods increases learning outcomes. There are many types of data that are concluded to solve this research problem is to use the method of the visiting teacher by using the tanjun teacher method. From the results of the study it can be concluded that by using the method of the teacher visiting in calculating comparisons and can increase the value of learners' learning outcomes in accordance with the expected goals and values through KKM
Demonstration of interiqr: Unobtrusive Edible Tags using Food 3D Printing

We demonstrate interiqr, a method that utilizes the infill parameter in the 3D printing process to embed information inside the food that is difficult to recognize with the human eye. Our key idea is to utilize the air space or secondary materials to generate a specific pattern inside the food without changing the model geometry. As a result, our method exploits the patterns that appear as hidden edible tags to store the data and simultaneously adds them to a 3D printing pipeline. Our demonstration includes the framework that connects the user with a data-embedding interface through the food 3D printing process, and the decoding system allows the user to decode the information inside the 3D printed food through backlight illumination and a simple image processing technique. We also demonstrate our method through the example application scenarios including the food interactivity and food traceability.
Sfitag: Efficient Software Fault Isolation with Memory Tagging for ARM Kernel Extensions

As ARM is becoming more popular in today’s processor market, the OS kernel on ARM is gradually bloated to meet the market demand for more sophisticated services by absorbing diverse kernel extensions. Since this kernel bloating inevitably increases the attack surface, there has been a continuous effort to decrease the surface by dissociating or isolating untrusted extensions from the kernel. One approach in this effort is using software fault isolation (SFI) that instruments memory and control-transfer instructions to prevent isolated extensions from having unauthorized accesses to memory regions of the core kernel. Being implementable in pure software has been considered the greatest strength of SFI and thus popularly adopted by engineers to isolate kernel extensions, but software versions of SFI mostly suffer from high performance overhead, which can be a critical drawback for performance-sensitive mobile devices that overwhelmingly use ARM CPUs. The purpose of our work, named as Sfitag, is to make SFI for ARM kernel extensions more efficient by leveraging the hardware support from the latest ARM AArch64 architecture, called the ARM8.5-A memory tagging extension (MTE). For efficiency, Sfitag relies on MTE support when it allocates a tag value different from the core kernel for untrusted extensions and enforces extensions to use that value as a tag for pointers and memory objects. Consequently, in Sfitag, accessing the core kernel memory is legitimate only when the tag of a pointer matches the value of the kernel tag, which by means of MTE in effect enables us to safely confine unexpected and buggy behaviors of extensions within the space isolated from the kernel. Through our evaluation, we prove the effectiveness of Sfitag by showing that our MTE-supported SFI efficiently enforces isolation for extensions just with 1% slowdown on the throughput of a network driver and 5.7% on a block device driver.
Pemanfaatan Museum Sumpah Pemuda sebagai Sumber Belajar melalui Teknologi Virtual Reality

This research aims to explore the utilization of the Youth Pledge Museum as a learning resource through Virtual Reality (VR) or Virtual Tour technology, and to understand the process of using VR in history education. The research adopts a qualitative method with a phenomenological approach. The focus of the study includes three main components: 1) Museum collections and their management, 2) The process of utilizing the Youth Pledge Museum collections as a source of historical learning through VR technology, and 3) The role of teachers in history education through VR technology. Data for the research were collected through observation, semi-structured interviews, and document analysis related to the Youth Pledge Museum and the use of VR in history education. This article describes the various collections held by the Youth Pledge Museum and the technologies that have been developed, including Virtual Reality (VR), which can be used in history education. The steps for using VR in history education include: 1) Selecting a relevant Virtual Reality museum for the learning material, 2) Preparing the necessary applications and equipment, 3) Setting up an Android phone, 4) Ensuring internet connectivity, 5) Finding the link to the Virtual Reality content of the Youth Pledge Museum, 6) Accessing the discovered link and starting the Virtual Tour. As a form of technology-integrated learning, the use of VR is expected to accommodate the development of 21st-century skills and encourage students to become active learners. This approach aligns with the student-centered learning concept applied in the independent curriculum, allowing students to construct their own knowledge.Keywords: museums, youth pledges, learning resources, Virtual Reality.
Responding to Articulation of Outcome-Based Education in Graduate Education by Cruz and Doctolero (2015); Insights from an Educational Leadership Major

RESEARCH" by Cruz and Doctolero (2015) from the perspective of an Educational Leadership major and research enthusiast.The authors' expertise and experience as practitioners enhance the relevance and practicality of their research findings on adopting outcome-based education in graduate programs.The need of using a double-feedback loop to overcome the discrepancy between professed values and theory-in-practice is emphasised in the paper.This correspondence article contributes to the ongoing conversation about the implementation and effectiveness of outcome-based education in graduate education, providing valuable insights and recommendations for the implementation of related studies in the future.
Based on Multi-Strategy Improved Harris Hawk Optimization Algorithm

The paper proposes the EHHO algorithm to address the issues of reduced population diversity due to the use of random strategies for initial populations during the exploration stage of the Harris Hawk optimization (HHO) algorithm, as well as the problem of being trapped in local optima during the iterative process due to linear changes in escape energy during control development and exploration. Firstly, the Tent mapping function is introduced during the exploration stage to initialize the HHO population position. Secondly, a new formula for updating the nonlinear escape energy is used. Finally, the cosine inertia weight is utilized to improve the prey position update formula, thereby enhancing the algorithm’s local optimization ability. Experimental results demonstrate that the three optimization strategies effectively enhance the optimization performance of the HHO algorithm on 12 benchmark functions, and the improved HHO algorithm is significantly superior to other compared optimization algorithms.
Anomaly Detection of Spent Fuel Safeguards Information Based on Machine Learning : A Comparison of performance using XGBoost and OCSVM

사용후핵연료를 장기적으로 관리하기 위해서 핵연료 제조정보, 연소 이력, 운반 및 저장 등의 다양한 안전정보가 필요하다. 오랜 기간, 다양한 단계를 거치며 생산되는 사용후핵연료 안전정보는 다수의 근무자에 의해 안전정보 데이터가 관리되면서 데이터가 누락되거나 부정확한 값이 포함될 수 있다. 이러한 인적 오류는 사용후핵연료 안전조치에 직접적인 영향을 미치므로 데이터의 품질을 정확히 관리하는 것이 필수적이다. 본 연구는 사용후핵 연료 안전정보 품질관리를 위해 머신러닝 기법 중 XGBoost 기반의 이상치 탐지를 수행한다. 기존에 사용되어 온 OCSVM과의 비교를 통해 XGBoost의 성능을 비교 분석한다. In order to manage the long term safeguards of spent fuel, various safety information such as nuclear fuel manufacturing information, burnup history, transport, and storage is required. The spent fuel safeguards information produced over a long period and through various stages may contain missing or incorrect values as multiple workers manage safety information data. Human error directly affects safeguards, so managing data quality is essential. This paper performs XGBoost-based anomaly detection using machine learning techniques for quality management of spent fuel safety information. In addition, we analyze the performance of XGBoost compared to the existing OCSVM.
Sea Ship Target Detection Based On Improved ViBe Algorithm

Aiming at the problems of slow “ghost image” elimination speed, ship trail interference, and complicated sea surface noise in the process of ViBe algorithm’s ship target detection in the sea background. This paper proposes a ViBeDiff3C object detection algorithm which combines the frame difference method and the improved ViBe algorithm. Based on the frame-to-frame difference method, the ViBe algorithm is improved to detect ship targets, and the “ghost” problem caused by the ViBe algorithm caused by moving targets in the first frame of the image is solved, thereby improving the update speed of the background template and achieving the goal of quickly eliminating “ghosts”. Objective To improve the detection effect of the frame difference method by introducing the Canny operator into the frame difference method, and solve the problems of sea surface noise, ship wakes and holes, thereby improving the clarity and accuracy of the target.
Efficiency Improvement of Mobile Robot Path Planning using Dingo Optimization Algorithm

When solving the problem of path planning method for mobile robot obstacle avoidance, the newly proposed Dingo optimization algorithm (DOA) is used for path planning, and the efficiency of path planning is improved by using the advantage of the dingo optimization algorithm with few parameters and fast convergence. At the same time, the path obtained in the grid map is deredundantly processed, and the path length is optimized, which shortens the path length to a certain extent and improves the accuracy of path planning. In order to verify the superiority of the dingo optimization algorithm in solving the problem of mobile robot path planning, it is combined with the wolf pack algorithm (GWO) for path planning on the grid map, and the result shows that the dingo optimization algorithm is more prominent in robot path planning And better adaptability.