Reviewerly/exHarmony huggingface dataset:
Research data sharing: Practices and perceptions of researchers at the selected universities in Tanzania

Despite the presence of multiple platforms for research data sharing such as data repositories, journal systems, social media, and portable devices, the practice of data sharing has not well been deployed in many universities in Africa including Tanzania. Therefore, this study investigated research data sharing at the selected universities in Tanzania. Specifically, the study sought to explore the research data sharing practices as well as determine the researchers’ perceptions of research data sharing. This study used a cross-section survey design, where both qualitative and quantitative research approaches were used. A sample size of 200 was drawn from a population of 1846 of the universities under study. Self-administered questionnaires were used to collect data from researchers while interviews were used to collect data from the university leaders. The findings indicate that researchers were practicing research data sharing in the surveyed institutions at different levels. The study has also revealed that researchers were mostly practicing sharing of research data within their research groups. Further, it was found that most of the respondents were willing to use others’ research data compared to their data sets being used by others. Moreover, the academic backgrounds of the researchers were significantly important in research data-sharing practices. The study also found that researchers perceived that sharing of research data could easily be done if researchers would benefit from sharing the research data or if researchers would be forced to share their research data. The study concludes that, researchers at the surveyed institutions have been practicing research data sharing although at the minimum level. The study recommends the establishment of research data-sharing national framework that can guide all entities and activities related to research data sharing.
ENHANCING STUDENTS’ VOCABULARY KNOWLEDGE TROUGH READING THEMATIC STORY AT SMPN 6 WATAMPONE

This study discusses reading thematic stories to increase students' vocabulary knowledge. This study used thematic stories as learning media. This study applied a pre-experimental method with a one group pre-test and post-test design. The population of this research is junior high school students from SMP Negeri 6 Watampone. The sample of this study consisted of 30 grade 7 students. This study used one instrument, namely the multiple-choice test. Data collection was carried out by conducting a pre-test and post-test to measure the development of students' vocabulary knowledge before and after treatment. As a result, it was revealed that there was a significant difference from the pre-test and post-test. The average pre-test score was 29.50 and the post-test average score was 79.83. From T-test the research found that the significant value (significant 2-tailed) value is lower than alpha (α) (0.000,05). It means that reading thematic story can improve students’ vocabulary knowledge.
83 Computational Modeling of Planning and Inhibition in the Tower of London

Objective: The Tower of London is commonly used to assess planning ability. Deficient outcomes may however have different causes: A participant may not have the ability to think a sufficient number of steps into the future, or may become, for example, impatient to evaluate different possible paths. Outcomes are thus not pure measures of the "planning" construct of primary interest, which may have contributed to findings of low reliability and low validity of these outcomes in the literature. The advent of computerized testing combined with computational modeling potentially allows to go beyond traditional outcomes such as "total number of moves" and "total time taken" and disentangle different processes that are of primary interest. The goal of the current study is to establish whether a model that consists of "planning ability" and "response inhibition" parameters can be used to describe Tower of London data. Participants and Methods: We constructed an algorithm that produces Tower of London data, and a computational model that uses every single decision of a participant as input (e.g., whether a participant moves the red or the blue ball to the right peg in setting 15 when trying to get to setting 28). There are 210 unique decision situations that participants can encounter. Our algorithm and Bayesian hierarchical model uses two parameters for each participant as well as a guessing rule, that together determine the participant's decision at every step. The appropriateness of the model was evaluated in a simulation study, where the simulated distribution of data implied by this model is compared to the empirical distribution of total number of moves observed in real datasets. Data were simulated for 10 items with a sample size of 200 participants. Results: Our simulation study shows that with our model the empirical distribution of total number of moves is successfully replicated in the distribution of the simulated data. Conclusions: Computational modeling provides a new window into Tower of London performance by identifying different processes. Modeling thus allows us to go beyond aspecific descriptions of planning ability. Furthermore, using the high-resolution data of computerized testing allows us to estimate these parameters reliably without requiring "big data", keeping participant burden low. This study will be followed up in three ways. First, predictions will be preregistered and tested for these new cognitive outcomes in several large oncological patient samples. Second, the model will be extended to include reaction times, to include an additional metric of cognitive computation. Third, the new cognitive process outcomes will be analyzed in conjunction with cognitive process outcomes on other tests to establish process communalities.
Application and Research Based on Random Forest Regression and CRITIC Methods

It is especially important to utilize existing data to enhance and monitor through a targeted and systematic approach, so this paper extracts some key data items based on valid data and analyzes their importance. Combining the random forest regression and CRITIC method to consider the importance, through the correlation heat map and the results of CRITIC for the initial screening of the characteristics, the use of weighting to get a comprehensive indicator to reflect the importance of the indicator. In addition, the sensitivity analysis yields that the model is more stable and can reasonably determine the key item data of the relevant research content, with a view to generating certain reference significance for the application and research in other fields.
Adaptive clustering object detection method for UAV images under long-tailed distributions

UAV images are characterized by small targets, difficult to identify in the background image, clustering and sparse distribution of targets, etc. Many researchers have proposed the clustering target detection method (ClusDet) for UAV images. However, due to the large differences in target scales and uneven distribution of targets in UAV images, showing long-tailed distribution, the traditional ClusDet algorithm tends to truncate large and medium targets in the process of clustering; in the detection process, the fixed-threshold NMS method in the ClusDet algorithm is difficult to adaptively detect targets of different sizes, clustering and mutual occlusion. To address the above problems, this paper proposes an adaptive clustered target detection algorithm based on UAV images under long-tail distribution. The method is divided into three sub-networks: the adaptive clustering sub-network, which outputs several segmented images of small target clustering regions by extracting potential small target clustering regions in UAV aerial images; the segmentation and filling sub-network, which fills the images with disproportionate aspect ratio for the output of the adaptive clustering network to keep the size of the images within the reasonable range required by the detection network; and the detection sub-network, which detects the targets within the reasonable range required by the detection network by introducing attention mechanism, using variable threshold NMS, and training using sample balancing strategy effectively improve the detection accuracy of targets in the clustered region. Trained in VisDrone 2019 dataset, the simulation results show that the UAV image adaptive clustering target detection method based on long-tailed distribution has a large improvement in the detection accuracy of small targets, and can effectively improve the detection accuracy of the model for targets in the aggregation region, while the model has good generalization ability.
The need analysis of PjBL Model Science e-book based indigenous knowledge on process of making ulos woven cloth

The need and availability of digital open materials such as e-books that contain original knowledge are very necessary in science learning, in order to create contextual learning. The basis for the need for e-books containing indigenous knowledge is the existence of various problems that occur in science learning. Some of the problems with science learning include, students still think it is difficult, science learning is irrelevant, uninteresting and monotonous, and students' comprehension ability is lacking. This research aims to find out what needs teachers and students want so that science learning can be more innovative. This research uses descriptive qualitative methods. The instruments used were interview guides, questionnaire sheets and science literacy tests. Data collection carried out in this research was interviews, questionnaires and scientific literacy tests. The sample involved in this research was 1 teacher and 53 students using a cluster random sampling technical
On the Formal Evaluation of the Robustness of Neural Networks and Its Pivotal Relevance for AI-Based Safety-Critical Domains

Survey/Review Study On the Formal Evaluation of the Robustness of Neural Networks and Its Pivotal Relevance for AI-Based Safety-Critical Domains Mohamed Ibn Khedher 1,*, Houda Jmila 2, and Mounim A. El-Yacoubi 2 1 IRT-SystemX, 2 Bd Thomas Gobert, Palaiseau 91120, France 2 Samovar, Telecom SudParis, Institut Polytechnique de Paris, 19 place Marguerite Perey, Palaiseau 91120, France * Correspondence: ibnkhedhermohamed@hotmail.com Received: 11 July 2023 Accepted: 31 October 2023 Published: 21 December 2023 Abstract: Neural networks serve as a crucial role in critical tasks, where erroneous outputs can have severe consequences. Traditionally, the validation of neural networks has focused on evaluating their performance across a large set of input points to ensure desired outputs. However, due to the virtually infinite cardinality of the input space, it becomes impractical to exhaustively check all possible inputs. Networks exhibiting strong performance on extensive input samples may fail to generalize correctly in novel scenarios, and remain vulnerable to adversarial attacks. This paper presents the general pipeline of neural network robustness and provides an overview of different domains that work together to achieve robustness guarantees. These domains include evaluating the robustness against adversarial attacks, evaluating the robustness formally and applying defense techniques to enhance the robustness when the model is compromised.
CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

In deep learning, model quality is extremely important. Consequently, the quality and the sufficiency of the datasets for training models have attracted considerable attention from both industry and academia. Automatic data augmentation, which provides a means of using image processing operators to generate data from existing datasets, is quite effective in searching for mutants of the images and expanding the training datasets. However, existing automatic data augmentation techniques often fail to fully exploit the potential of the data, failing to balance the search efficiency and the model accuracy. This paper presents CAugment, a novel approach to diversifying image datasets by combining image processing operators. Given a training image dataset, CAugment is composed of: 1) the three-level evolutionary algorithm (TLEA) that employs three levels of atomic operations for augmenting the dataset and an adaptive strategy for decreasing granularity and 2) a design that uses the three-dimensional evaluation method (TDEM) and a dHash algorithm to measure the diversity of the dataset. The search space can be expanded, which further improves model accuracy during training. We use CAugment to augment the CIFAR-10/100 and SVHN datasets and use the augmented datasets to train the WideResNet and Shake-Shake models. Our results show that the amount of data increases linearly along with the training epochs; in addition, the models trained by the CAugment-augmented datasets outperform those trained by the datasets augmented by the other techniques by up to 17.9% in accuracy on the SVHN dataset.
A Camera Optimal Projection Model Identification and Calibration Method Based on the NGO-BA Architecture

In order to bridge the fundamental commonalities between imaging models of camera lenses with different principles and structures, allowing for an accurate description of imaging characteristics across a wide range of field-of-view (FOV), we have proposed a generic camera calibration method on the basis of the projection model optimization strategy. First, in order to cover the current mainstream projection models, piecewise functions for geometric projection models and a polynomial function for the fitting projection model are designed. Then, the corresponding camera multistation self-calibration bundle adjustment (BA) module is developed for various projection models. Further, by integrating the self-calibration BA algorithm into the northern goshawk optimization architecture, iterative optimization is performed on the projection model adjustment parameters, camera interior parameters, camera exterior parameters, and lens distortion parameters until the target reprojection (RP) error reaches the global minimum. The experimental results indicate that the calibration RP root mean square error in this method is 1/20 pixel for a 68° FOV camera, 1/13 pixel for an 84° FOV camera, 1/9 pixel for a 115° FOV camera, 1/9 pixel for a 135° FOV camera, and 1/6 pixel for a 180° FOV camera. This calibration method offers fast and versatile optimization for various projection model types, encompassing a wide range of projection functions. It can efficiently determine the optimal projection model and all imaging parameters for multiple cameras during the calibration process.
Compositional Vector Semantics in Spiking Neural Networks

Categorical compositional distributional semantics is an approach to modelling language that combines the success of vector-based models of meaning with the compositional power of formal semantics. However, this approach was developed without an eye to cognitive plausibility. Vector representations of concepts and concept binding are also of interest in cognitive science, and have been proposed as a way of representing concepts within a biologically plausible spiking neural network. This work proposes a way for compositional distributional semantics to be implemented within a spiking neural network architecture, with the potential to address problems in concept binding, and give a small implementation. We also describe a means of training word representations using labelled images.
Highly Available Blockchain Nodes With N-Version Design

As all software, blockchain nodes are exposed to faults in their underlying execution stack. Unstable execution environments can disrupt the availability of blockchain nodes' interfaces, resulting in downtime for users. This paper introduces the concept of N-Version Blockchain nodes. This new type of node relies on simultaneous execution of different implementations of the same blockchain protocol, in the line of Avizienis' N-Version programming vision. We design and implement an N-Version blockchain node prototype in the context of Ethereum, called N-ETH. We show that N-ETH is able to mitigate the effects of unstable execution environments and significantly enhance availability under environment faults. To simulate unstable execution environments, we perform fault injection at the system-call level. Our results show that existing Ethereum node implementations behave asymmetrically under identical instability scenarios. N-ETH leverages this asymmetric behavior available in the diverse implementations of Ethereum nodes to provide increased availability, even under our most aggressive fault-injection strategies. We are the first to validate the relevance of N-Version design in the domain of blockchain infrastructure. From an industrial perspective, our results are of utmost importance for businesses operating blockchain nodes, including Google, ConsenSys, and many other major blockchain companies.
Image Reconstruction by VAE

Variational Autoencoders (VAEs) have become a recognized tool for learning data distributions in various applications. Building on previous research, this paper investigates image reconstruction by incorporating Planar Flows into VAEs. As a form of Normalizing Flows, Planar Flows potentially aid VAEs in modeling more intricate distributions, which may enhance the quality of reconstructed images. Our study employs a well-established architecture that integrates Planar Flows with VAEs. Experiments on MNIST are provided to compare our replication efforts with traditional VAEs. The results suggest that utilizing Planar Flows can offer certain improvements in image reconstruction. This work further solidifies the understanding of the combined use of VAEs and Planar Flows in image reconstruction tasks.
Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this paper, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.
A Robust Detection and Correction Framework for GNN-Based Vertical Federated Learning

Graph Neural Network based Vertical Federated Learning (GVFL) facilitates data collaboration while preserving data privacy by learning GNN-based node representations from participants holding different dimensions of node features. Existing works have shown that GVFL is vulnerable to adversarial attacks from malicious participants. However, how to defend against various adversarial attacks has not been investigated under the non-i.i.d. nature of graph data and privacy constraints. In this paper, we propose RDC-GVFL, a novel two-phase robust GVFL framework. In the detection phase, we adapt a Shapley-based method to evaluate the contribution of all participants to identify malicious ones. In the correction phase, we leverage historical embeddings to rectify malicious embeddings, thereby obtaining accurate predictions. We conducted extensive experiments on three well-known graph datasets under four adversarial attack settings. Our experimental results demonstrate that RDC-GVFL can effectively detect malicious participants and ensure a robust GVFL model against diverse attacks. Our code and supplemental material is available at https://github.com/zcyang-cs/RDC-GVFL.
Unsupervised Domain Adaptation for 3D Object Detection via Self-Training

3D object detection based on point clouds plays a crucial role in autonomous driving. High quality detection results can provide reliable basis for subsequent stages such as trajectory prediction and path planning. Although many advanced 3D object detectors currently exist, when employing them to another domain, there is often a huge performance drop. In addition, existing domain adaptation methods for 3D object detection only focus on one or two observable factors such as scale mismatch and density variation that lead to domain shift and they do not take some invisible factors (weather, road condition and sensor type, etc.) into account. Therefore, we attempt to propose a self-training pipeline for unsupervised domain adaptation on 3D object detection. Firstly, we pretrain the detectors with a specific data processing paradigm which includes object random scaling, random beam re-sampling and etc. Then, we employ mean-teacher framework which includes cross-domain student model and target-only teacher model. We employ adversarial learning in student model, enforcing the student model to learn domain-invariant features. This process could further eliminate the invisible factors that lead to domain shift. Furthermore, in order to further obtain high-quality pseudo labels, we apply different data augmentation strategy and mutual learning between student model and teacher model. In addition, we adopt domain statistics normalization to ensure a stable training behavior. Extensive experiments under three different adaptation tasks demonstrate the effectiveness of our method.
BERT-Based Neural Network for Inpatient Fall Detection from Electronic Medical Records: A Retrospective Cohort Study (Preprint)

Background: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls. Objective: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model. Methods: A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture. Results: To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code–based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings. Conclusions: The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.