Reviewerly/exHarmony huggingface dataset:
Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model

Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.
Tswinpose: Enhanced monocular 3D human pose estimation with JointFlow

Monocular estimation of 3D human poses is challenging due to ambiguity in depths and partial occlusion. Most recent works define this as a 2D-to-3D lifting task, taking 2D key point sequences and using spatial and temporal relationships. However, prior works focus on capturing spatio-temporal correlations but ignore the motion of joints that is needed for continuous estimation. To extend the potential of 2D-to-3D pose estimation, we propose TSwinPose, which learns multi-scale spatio-temporal representations from 2D key point locations and patterns of motion. The input 2D key point sequences are enhanced by JointFlow, which encodes the motion of each human joint. Based on Swin-Transformer, we designed a temporal domain Swin-Unet structure to model multi-scale spatio-temporal relationships of human joints across different temporal windows. The final 3D pose generated by multi-stage representations is consistent temporally and has a higher accuracy. Experiments conducted on three benchmark datasets, Human3.6M, MPI-INF-3DHP, and HumanEva-I, demonstrate that TSwinPose achieves performance that is on par with state-of-the-art methods. Moreover, the introduction of JointFlow as a plug-in extension enhances performance significantly, particularly benefiting long-term 2D-to-3D lifting human pose estimation methods.
Secure decentralized firmware update delivery service for Internet of Things

With the increasing number of Internet of Things devices, it is crucial to keep them up-to-date to prevent cyber attacks. Traditional centralized delivery is not suitable for scaling and also can be too expensive to run for small vendors. Thus, finding a fully secure, scalable, yet cost-efficient firmware update distribution strategy is always an open research problem. This paper tries to answer those problems by proposing Patchman, a secure decentralized firmware update delivery service for the Internet of Things ecosystem leveraging blockchain. When a new firmware patch is available, vendors make a bid in the smart contract for anyone to join as firmware distributors. For each successful delivery to targeted devices, distributors are rewarded with tokens. Meanwhile, devices gain a reputation score every time they successfully install an update. To ensure robustness and fairness, we develop secure fair exchange protocols using verifiable proof-of-delivery and proof-of-installation. Those proofs can be traded in the blockchain for rewards and reputation scores increase, proving that the proof-holders have successfully processed a firmware delivery and firmware installation task. This way, the firmware update delivery can be executed safely without centralized third-party control. Our evaluation results show that our implementation complies with the five security goals that we envision. We also have successfully punished malicious actions by confiscating their deposits and requiring them to pay up to four times of base deposit value when they join the next update task, ensuring the fairness of our protocol. Furthermore, we generate low processing delay overhead compared to existing works that rely on Zero-Knowledge Proofs. The gas usage consumption from our approach also produced a competitive result despite our works supporting more features than existing works, ensuring the efficiency of our proposal.
Applying large language models and chain-of-thought for automatic scoring

This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written responses to science assessments. We focused on overcoming the challenges of accessibility, technical complexity, and lack of explainability that have previously limited the use of artificial intelligence-based automatic scoring tools among researchers and educators. With a testing dataset comprising six assessment tasks (three binomial and three trinomial) with 1650 student responses, we employed six prompt engineering strategies to automatically score student responses. The six strategies combined zero-shot or few-shot learning with CoT, either alone or alongside item stem and scoring rubrics, developed based on a novel approach, WRVRT (prompt writing, reviewing, validating, revising, and testing). Results indicated that few-shot (acc = 0.67) outperformed zero-shot learning (acc = 0.60), with 12.6% increase. CoT, when used without item stem and scoring rubrics, did not significantly affect scoring accuracy (acc = 0.60). However, CoT prompting paired with contextual item stems and rubrics proved to be a significant contributor to scoring accuracy (13.44% increase for zero-shot; 3.7% increase for few-shot). We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks. We also found that GPT-4 demonstrated superior performance over GPT -3.5 in various scoring tasks when combined with the single-call greedy sampling or ensemble voting nucleus sampling strategy, showing 8.64% difference. Particularly, the single-call greedy sampling strategy with GPT-4 outperformed other approaches. This study also demonstrates the potential of LLMs in facilitating explainable and interpretable automatic scoring, emphasizing that CoT enhances accuracy and transparency, particularly when used with item stem and scoring rubrics.
Search Space Division Method for Wrapper Feature Selection on High-Dimensional Data Classification

Feature selection (FS) is an essential pre-processing technique for high-dimensional data. Wrapper-based FS techniques are known for their superior performance over filter FS. However, when the dimensionality of data is very high the wrapper techniques become computationally very expensive. To solve this problem of scalability, this paper proposes the concept of search space division (SSD) which leads to smaller search spaces and hence reduced computational cost. The proposed SSD approach is generic in nature and can be integrated with any wrapper-based FS technique. The SSD approach divides search space into multiple parts and the wrapper-based FS is independently applied to each part. To facilitate the interaction of features over the complete search space, all feature subsets obtained from each part are combined and the wrapper-based FS is again applied to get the final feature subset. Moreover, a new wrapper FS technique named Binary Rao (BRAO) Algorithm has been proposed. BRAO is based on the metaphor-less, parameter-less meta-heuristic optimization algorithm Rao-1. The proposed combination of SSD and BRAO algorithm named SSDR has produced better classification accuracies with a smaller number of features in a much shorter time as compared to other well-regarded wrapper FS techniques as well as classical FS techniques over thirteen benchmark high-dimensional datasets.
How to create a successful mobile learning strategy for medical education during lockdowns?

BACKGROUND: Mobile learning has played an important role during the COVID-19 pandemic and medical schools now consider it as an effective educational method in current and future crises. In this qualitative study, an attempt was made to demonstrate the principles of designing a mobile learning strategy in medical education from the perspective of experts. MATERIALS AND METHODS: The study was conducted by the qualitative content analysis method. The data were collected from July 2022 to Feb 2023. Twelve participants were included in this study from Iran’s medical universities, consisting of two members of the Higher Council of Virtual Education, three educational directors, three clinical faculty members, two faculty members specializing in e-learning and medical education, an educational vice, and a dean. Data were collected using semi-structured interviews and analyzed by Granheim and Lundman’s (2004) method. RESULTS: Out of twelve participants in the study, eight (66%) were males and four (44%) females. Data were classified into eight categories and one theme. Based on the participants’ experiences, the main theme, that is, “the principles of medical education design in mobile learning,” included pedagogical component, interactive design, effective and comprehensive analysis, achieving objectives with the mobile learning platform, generating micro- and interactive e-content, teaching-learning interactive methods, course implementation and interactive evaluation at both micro- and macro-levels. CONCLUSION: Data analysis revealed that in addition to the eight principles in the medical education design in mobile learning, the participants prioritized the two principles of pedagogical component and interactive design over other principles in educational design. Using a successful mobile learning strategy in situations of restrictions limiting physical presence may improve the quality of medical education.
Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis

Abstract Introduction Alzheimer's disease (AD) is a progressive brain disorder that impairs cognitive functions, behavior, and memory. Early detection is crucial as it can slow down the progression of AD. However, early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests. Methods This study introduces a data acquisition technique and a preprocessing pipeline, combined with multivariate long short‐term memory (M‐LSTM) and AdaBoost models. These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients, using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database. Results The methodology proposed in this study significantly improved performance metrics. The testing accuracy reached 80% with the AdaBoost model, while the M‐LSTM model achieved an accuracy of 82%. This represents a 20% increase in accuracy compared to a recent similar study. Discussion The findings indicate that the multivariate model, specifically the M‐LSTM, is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.
Comparative analysis of various models for image classification on Cifar-100 dataset

Abstract Nowadays, people developed various convolutional neural network (CNN) based models for computer vision. Some famous models, such as GoogLeNet, Residual Network (ResNet), Visual Geometry Group (VGG), and You Only Look Once (YOLO), have different architecture and performances. Determining which model to use may be a troublesome problem for those just starting to study image classification. To solve this problem, we introduce the GoogLeNet, ResNet-18, and VGG-16 models, comparing their architecture, features, and performance. Then we give our suggestions based on the test results to help beginners choose a suitable model. We conducted experiments to train and test GoogLeNet, ResNet-18, and VGG-16 on the Cifar-100 datasets with the same hyperparameters. Based on the test results (test accuracy, average test loss, training loss), we analyze the figures for trends, key points, increase rate, and other features. Then we combine the architecture of each model to make our conclusions. The experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time complexity. ResNet-18 also has the fastest convergence speed. GoogLeNet would be the second choice because it functions similarly to ResNet-18 and is even better. However, training GoogLeNet is a time-consuming task. VGG is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18.
Decentralized Face Identification with Hierarchical Navigable Small World on Blockchain

Abstract This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.
Graph sampling based deep metric learning for cross-view geo-localization

Abstract Cross-view geo-localization has emerged as a novel computer vision task that has garnered increasing attention. This is primarily attributed to its practical significance in the domains of drone navigation and drone-view localization. Moreover, the work is particularly demanding due to its inherent requirement for cross-domain matching. There are generally two ways to train a neural network to match similar satellite and drone-view images: presentation learning with classifiers and identity loss, and metric learning with pairwise matching within mini-batches. The first takes extra computing and memory costs in large-scale learning, so this paper follows a person-reidentification method called QAConv-GS, and implements a graph sampler to mine the hardest data to form mini-batches, and a QAConv module with extra attention layers appended to compute similarity between image pairs. Batch-wise OHEM triplet loss is then used for model training. With these implementations and adaptions combined, this paper significantly improves the state of the art on the challenging University-1652 dataset.
Research on laser-based mobile robot SLAM and autonomous navigation

Abstract SLAM (Simultaneous Localization and Mapping) and autonomous navigation systems, as fundamental components of mobile robots, largely determine their ability to accomplish tasks. While research on laser-based SLAM and autonomous navigation algorithms in simulated environments has achieved significant success, there is limited research on deploying these algorithms on physical mobile robots, conducting real-world experiments to provide practical data for validating the accuracy and applicability of theoretical models. This paper begins by presenting the system architecture of the mobile robot used in this study. Subsequently, it delves into the research on SLAM algorithms, analyzing and selecting the Gmapping algorithm as the solution to the SLAM problem. Furthermore, the paper introduces the research on autonomous navigation, providing a detailed explanation of the principles behind the A* global path planning algorithm combined with the Dynamic Window Approach (DWA) for local path planning. Finally, by controlling the mobile robot through a distributed communication network, the paper tests the performance of the mobile robot, constructs maps in a real indoor environment, achieving a relative map accuracy error of less than 3%, thus validating the effectiveness of the Gmapping algorithm for practical mapping. Autonomous navigation tasks are also performed, testing the mobile robot’s local obstacle avoidance capabilities using temporary obstacles. Overall, this research demonstrates that the designed mobile robot can effectively accomplish SLAM and autonomous navigation tasks. The results of this study can also assist beginners in rapidly and clearly building physical mobile robots and deploying SLAM systems.
Hyperledger sawtooth based supplychain traceability system for counterfeit drugs

Drug supply chains have been facing severe issues as counterfeit product cases are increasing exponentially. A supply chain management system that ensures transparency, reliability, and provenance of drugs would increase the trustworthiness of the whole industry. One solution to all three needs is utilizing blockchain-based distributed ledger technologies (DLTs). Even though DLTs are emerging as an ideal infrastructure for multi-stakeholder supply chain applications, they still need to be more mature to address the specific challenges specific to each use case. In this article, we propose a distributed blockchain-based framework, PHTrack, leveraging hyperledger sawtooth as a drug supply chain traceability system. Hyperledger sawtooth addresses scalability issues by offering a robust foundation to support large-scale drug supply chain operations in a modular way for it’s each participating stakeholder. Furthermore, it simplifies the integration process with existing systems, even those employing different technologies, thereby facilitating a smoother transition to DLTs. The design of PHTrack is oriented towards minimizing resource consumption throughout the process, particularly within hyperledger sawtooth nodes. Additionally, it incorporates quantum secure off-chain communication for peer-to-peer (P2P) communication. A set of experiments was conducted to validate the proposed framework. Experiments have shown that PHTrack provides reliable and comprehensive drug provenance as well as real-time drug supply chain tracking.
Multi-level multilingual semantic alignment for zero-shot cross-lingual transfer learning

Recently, cross-lingual transfer learning has attracted extensive attention from both academia and industry. Previous studies usually focus only on the single-level alignment (e.g., word-level, sentence-level), based on pre-trained language models. However, it leads to suboptimal performance in downstream tasks of the low-resource language due to the missing correlation of hierarchical semantic information (e.g., sentence-to-word, word-to-word). Therefore, in this paper, we propose a novel multi-level alignment framework, which hierarchically learns the semantic correlation between multiple levels by leveraging well-designed alignment training tasks. In addition, we devise an attention-based fusion mechanism (AFM) to infuse semantic information from high levels. Extensive experiments on mainstream cross-lingual tasks (e.g., text classification, paraphrase identification, and named entity recognition) demonstrate the effectiveness of our proposed method, and also show that our model achieves state-of-the-art performance across various benchmarks compared to other strong baselines.
FCSG-Miner: Frequent closed subgraph mining in multi-graphs

Graph data-based mining is vital in various fields, such as business management, chemistry, and social networks. Frequency-based frameworks have limitations regarding large mining output. In many real-world scenarios, not all frequent patterns hold significant meaning. To address this issue, concise representation such as closed patterns is proposed. Unfortunately, existing methods utilize support or occurrence as frequency measurements, which have drawbacks. Support overlooks the isomorphic quantity of a single graph, while occurrence lacks the downward closure property. In this paper, we introduce an approach for mining frequent closed subgraphs. A novel measure MMNI (multiple minimum node image) is introduced to strike a balance between support and occurrence measures. Additionally, we design a novel structure to store occurrence information. We develop a pruning strategy and employ an early termination strategy to enhance efficiency. To evaluate the performance of our algorithm, we conduct experiments on seven real datasets, considering four aspects. The results demonstrate that our algorithm has high efficiency and performance compared to the state-of-the-art algorithm for closed subgraph mining. In several cases, our method requires only 50% of the time consumed by previous approaches. We also present a real-world application example in the domain of bike-sharing systems.