Reviewerly/exHarmony huggingface dataset:
Public administration using blockchain technology and platforms: new opportunities

This article aims to identify potential applications of blockchain technology in public administration. Although blockchain is most useful when trust in government is low (because blockchain provides greater accountability), the feasibility of implementing blockchain solutions depends on a certain level of administrative and technical capacity of the authorities and, most importantly, on their readiness and willingness to implement these technologies. depends on the following factors. The reluctance to implement blockchain technology is partly related to the perception that blockchain is in “competition” with the state. There are also “usual” political barriers to change in administrative structures, such as the reluctance of government officials to introduce new technologies. The analysis showed that blockchain technology has not yet become widespread in the public sector, although a number of pilot programs have been developed. In addition, many applications of blockchain have not progressed beyond proof of concept and pilot implementations on a limited scale, and further research is needed on the barriers to the adoption of blockchain technology in the public sector, including the issue of the so-called “digital divide”. We know that the consideration of blockchain in public administration can be divided into one or more of the following perspectives: blockchain as a technology of freedom, blockchain as a polycentric structure, or blockchain as a new type of database. Each perspective sheds light on its potential for public sector governance, as blockchain can be all three, depending on the political, economic, social and technological context. The article also identifies that personal data, government procurement, online government services and support for democratic processes are areas where blockchain technology can be successfully used to improve administrative efficiency. However, the analysis also proved that blockchain offers opportunities in almost all areas of public administration, so a special program for implementing blockchain technology in the public sector is needed.
An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation

Cardiac arrest is a sudden loss of heart function with serious consequences. In developing countries, healthcare professionals use clinical documentation to track patient information. These data are used to predict the development of cardiac arrest. We published a dataset through open access to advance the research domain. While using this dataset, our work revolved around generating and utilizing synthetic data by harnessing the potential of synthetic data vaults. We conducted a series of experiments by employing state-of-the-art machine-learning techniques. These experiments aimed to assess the performance of our developed predictive model in identifying the likelihood of developing cardiac arrest. This approach was effective in identifying the risk of cardiac arrest in in-patients, even in the absence of electronic medical recording systems. The study evaluated 112 patients who had been transferred from the emergency treatment unit to the cardiac medical ward. The developed model achieved 96% accuracy in predicting the risk of developing cardiac arrest. In conclusion, our study showcased the potential of leveraging clinical documentation and synthetic data to create robust predictive models for cardiac arrest. The outcome of this effort could provide valuable insights and tools for healthcare professionals to preemptively address this critical medical condition.
Robust Commonsense Reasoning Against Noisy Labels Using Adaptive Correction

Commonsense reasoning based on knowledge graphs (KGs) is a challenging task that requires predicting complex questions over the described textual contexts and relevant knowledge about the world. However, current methods typically assume clean training scenarios with accurately labeled samples, which are often unrealistic. The training set can include mislabeled samples, and the robustness to label noises is essential for commonsense reasoning methods to be practical, but this problem remains largely unexplored. This work focuses on commonsense reasoning with mislabeled training samples and makes several technical contributions: 1) we first construct diverse augmentations from knowledge and model, and offer a simple yet effective multiple-choice alignment method to divide the training samples into clean, semi-clean, and unclean parts; 2) we design adaptive label correction methods for the semi-clean and unclean samples to exploit the supervised potential of noisy information; and 3) finally, we extensively test these methods on noisy versions of commonsense reasoning benchmarks (CommonsenseQA and OpenbookQA). Experimental results show that the proposed method can significantly enhance robustness and improve overall performance. Furthermore, the proposed method is generally applicable to multiple existing commonsense reasoning frameworks to boost their robustness. The code is available at https://github.com/xdxuyang/CR_Noisy_Labels.
KONDISI MOTIVASI BELAJAR SISWA SELAMA IMPLEMENTASI PEMBELAJARAN JARAK JAUH DI TK SABILULUNGAN KOTA BANDUNG

This research analyzes the condition of students' learning motivation during the implementation of distance learning in Sabilulungan Mekar Rahayu Kindergarten, Bandung City. This research was studied because there were problems regarding distance learning being implemented. The research objectives are to examine the condition of students' learning motivation, the implementation of distance learning and the strategies of teachers and parents in implementing distance learning. The research approach uses a descriptive approach with qualitative methods. Research respondents are parents of students, data collection techniques using interviews, documentation and observation. The results showed that the condition of students' motivation tended to be less enthusiastic and felt bored, while the implementation of distance learning using whatsapp groups and learning activities had been designed and referred to the RPPH and RPPM, PAUD educators' strategies at home (parents) in conducting guidance in distance learning. namely using discussion strategies (question-and-answer) and exemplary while the strategy carried out by the teacher is to use a reward strategy given to children to foster learning motivation and carry out regular zoom.
Cognitive Model of Library Management in Crisis Conditions

Objective. The article considers library management as a complex socio-communication system in conditions of uncertainty of the external environment. The essence and methods of cognitive library management as an effective means of overcoming instability and global challenges of the XXI century are substantiated. Methods. With the help of information analysis, the peculiarities of the cognitive approach to library management in a crisis situation were defined. Results. A conclusion is made regarding the effectiveness of cognitive analysis, in particular PEST and SWOT analysis, cognitive structuring technologies. Cognitive analysis is considered as a powerful tool for researching a certain state of the library in an unstable and poorly structured environment. The application of the cognitive modeling method was used to identify the components of library management in conditions of uncertainty. Conclusions. Attention is focused on the need to use cognitive modeling, which contributes to a better understanding of the problem situation, detection of contradictions and qualitative analysis of the library as a system. A cognitive model of library management based on knowledge application technology in the management and decision-making process and effective communication interaction is proposed.
Implementation of Crossword Puzzle Learning Media in Indonesian Language Learning at Islamic Elementary School

The purpose of this research is to introduce learning media in the form of crossword puzzles at MIN 08 Medan City and assess the impact of this implementation. The qualitative research method was applied, employing a case study approach. The research data is in the form of MIN 08 Medan City applying learning media in the form of Crossword Puzzles. Data collection methods include observation, interviews, and documentation. Data analysis is accomplished by data reduction, data display, and generating conclusions. The findings revealed that MIN 08 in Medan benefited from the use of Crossword Puzzle media in learning Indonesian. This can be seen from the changes that occur in students both when learning is carried out and the results of implementing learning using the media. When learning is in progress, students are more enthusiastic about learning, more focused, and more active in learning. As for the learning outcomes, students who study using Crossword Puzzle media have higher scores than those without using Crossword Puzzle media.
MIL-Mixer: A Robust Bag Encoding Strategy for Multiple Instance Learning (MIL) using MLP-Mixer

This paper presents a robust bag encoding strategy based on an MLP mixer. The proposed approach introduces the mixing concept to MIL applications, which helps to generate robust bag encoding. The existing bag encoding strategies for MIL applications consider instances in the bag as independent. This assumption may restrict the performance of these algorithms. Therefore, this paper proposes MIL-Mixer, which utilizes the information between the instances to generate a robust bag encoding. We also extend MLP-Mixture to use classification token similar to vision transformers which diversify the encoding generation process. In this study, three benchmark MIL datasets are used to assess the performance of the proposed MIL-Mixer. In comparison with existing MIL approaches, the proposed MIL-Mixer achieves better performance.
Assessing the Sustainability of Bitcoin Mining: Comparative Review of Renewable Energy Sources

This study conducts a comprehensive assessment of Bitcoin mining's sustainability, concentrating on the incorporation of renewable energy sources such as hydroelectric, wind, solar, and geothermal power. It investigates the adaptability and potential constructive collaboration of these energy systems in the context of the Bitcoin mining scenario. This research presents a detailed understanding of the possibilities and challenges involved with integrating hydro, wind, solar, and geothermal resources into sustainable Bitcoin mining processes by methodically examining energy efficiency, environmental effect, and economic viability. The study contributes to the discussion on establishing a sustainable future for digital currencies by exposing significant connections between renewable energy technology and the cryptocurrency industry. To achieve the stated goals, the study carefully examines the energy usage strongly associated with Bitcoin mining and transaction processing. It investigates the elements that contribute to Bitcoin's energy consumption, addressing the environmental consequences of its energy-intensive activities. The study aims to find feasible techniques for reducing Bitcoin's carbon footprint, to cultivate a thorough understanding of the complex relationship between the digital currency revolution and its environmental consequences. It emphasizes the importance of balancing technical developments with environmental responsibilities. The concept contributes to establishing a conscious and sustainable future for the digital currency ecosystem by addressing the numerous components of Bitcoin's energy usage and advocating a collective commitment to protecting the environment in the face of advancing technology paradigms.
A Study on the Major Class of Physical Education Department according to Teaching and Learning Types

The purpose of this study is to look at undergraduate classes in physical education before and after COVID-19 from the perspective of professors and students. The purpose of this study had two folds: 1)to understandthe perspective of professors and students about physical education classes before and after COVID 19, and to provide a direction for the future of physical education classes. He then tried to find and present a direction for major classes in a rapidly changed environment. The research method applied the case study method, and three physical education professors and five major students were finally selected as research participants using intentional sampling and convenience sampling. As a result of the study, first, it was found that instructors dealt with instructional design more heavily in an online environment. Second, it was confirmed that the students actively participated in online lectures rather than offline lectures. This is interpreted as a result of learning self-directed learning during school days, so you have learned how to use your given time appropriately. Therefore it is necessary to prepare for online non-face-to-face education that may occur due to the global pandemic like COVID-19 in the future regarding the design of undergraduate classes in physical education.
Software Quality Measurement for Functional Suitability, Performance Efficiency, and Reliability Characteristics Using Analytical Hierarchy Process

The quality model used in this paper is ISO 25010. Functional Suitability, Performance Efficiency, and Reliability are the characteristics to be used. The case study used is the ITS Academic Information System, and the method used for the basis of calculation is the AHP (Analytical Hierarchy Process) method. The initial stage is to make a list of questionnaire questions, which are then filled out by three stakeholders: experts, students, and developers. With the AHP method, experts will analyze the questionnaire results to determine the required weight. This weight is used to calculate the quality of the software. There are two types of software measurements: student questionnaires and developer questionnaires. These two questionnaires become data input. Automatic measurements are carried out on Time Behavior aspects, namely Response Time Testing. In the automatic measurement stage, the URL to be tested by the tester is used as data input. From this automatic measurement, we experimented with the response time of the destination URL to respond to requests and conversion results on a scale of one hundred. The final value of these two types of measurements will be used in several equations to get the final value of the quality of the software. The study results are in the form of automatic measuring instruments of software quality. The measurement results can be used as feedback in making improvements so that the quality value increases when measured. Regarding Functional Suitability, the ITS Academic Information System has provided features according to user needs. In the aspect of Performance Efficiency, the ITS Academic Information System can provide performance and performance according to user needs. Meanwhile, regarding reliability, the ITS Academic Information System can carry out a function under certain conditions and times
Structure Invariant Transformation for better Adversarial Transferability

Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial examples, there is an urgent need for an effective adversarial attack to identify the deficiencies of DNNs in security-sensitive applications. As one of the prevalent black-box adversarial attacks, the existing transfer-based attacks still cannot achieve comparable performance with the white-box attacks. Among these, input transformation based attacks have shown remarkable effectiveness in boosting transferability. In this work, we find that the existing input transformation based attacks transform the input image globally, resulting in limited diversity of the transformed images. We postulate that the more diverse transformed images result in better transferability. Thus, we investigate how to locally apply various transformations onto the input image to improve such diversity while preserving the structure of image. To this end, we propose a novel input transformation based attack, called Structure Invariant Transformation (SIA), which applies a random image transformation onto each image block to craft a set of diverse images for gradient calculation. Extensive experiments on the standard ImageNet dataset demonstrate that SIA exhibits much better transferability than the existing SOTA input transformation based attacks on CNN-based and transformer-based models, showing its generality and superiority in boosting transferability. Code is available at https://github.com/xiaosen-wang/SIT.
Parallax-Tolerant Unsupervised Deep Image Stitching

Traditional image stitching approaches tend to leverage increasingly complex geometric features (e.g., point, line, edge, etc.) for better performance. However, these hand-crafted features are only suitable for specific natural scenes with adequate geometric structures. In contrast, deep stitching schemes overcome adverse conditions by adaptively learning robust semantic features, but they cannot handle large-parallax cases.To solve these issues, we propose a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. It provides accurate alignment for overlapping regions and shape preservation for non-overlapping regions by joint optimization concerning alignment and distortion. Subsequently, to improve the generalization capability, we design a simple but effective iterative strategy to enhance the warp adaption in cross-dataset and cross-resolution applications. Finally, to further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks. Compared with existing methods, our solution is parallax-tolerant and free from laborious designs of complicated geometric features for specific scenes. Extensive experiments show our superiority over the SoTA methods, both quantitatively and qualitatively. The code is available at https://github.com/nie-lang/UDIS2.
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network

This paper presents a novel network structure with illumination-aware gamma correction and complete image modelling to solve the low-light image enhancement problem. Low-light environments usually lead to less informative large-scale dark areas, directly learning deep representations from low-light images is insensitive to recovering normal illumination. We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks, which enables the correction factor gamma to be learned in a coarse to elaborate manner via adaptively perceiving the deviated illumination. Because exponential operation introduces high computational complexity, we propose to use Taylor Series to approximate gamma correction, accelerating the training and inference speed. Dark areas usually occupy large scales in low-light images, common local modelling structures, e.g., CNN, SwinIR, are thus insufficient to recover accurate illumination across whole low-light images. We propose a novel Transformer block to completely simulate the dependencies of all pixels across images via a local-to-global hierarchical attention mechanism, so that dark areas could be inferred by borrowing the information from far informative regions in a highly effective manner. Extensive experiments on several benchmark datasets demonstrate that our approach outperforms state-of-the-art methods.
FS-DETR: Few-Shot DEtection TRansformer with prompting and without re-training

This paper is on Few-Shot Object Detection (FSOD), where given a few templates (examples) depicting a novel class (not seen during training), the goal is to detect all of its occurrences within a set of images. From a practical perspective, an FSOD system must fulfil the following desiderata: (a) it must be used as is, without requiring any fine-tuning at test time, (b) it must be able to process an arbitrary number of novel objects concurrently while supporting an arbitrary number of examples from each class and (c) it must achieve accuracy comparable to a closed system. Towards satisfying (a)-(c), in this work, we make the following contributions: We introduce, for the first time, a simple, yet powerful, few-shot detection transformer (FS-DETR) based on visual prompting that can address both desiderata (a) and (b). Our system builds upon the DETR framework, extending it based on two key ideas: (1) feed the provided visual templates of the novel classes as visual prompts during test time, and (2) "stamp" these prompts with pseudo-class embeddings (akin to soft prompting), which are then predicted at the output of the decoder. Importantly, we show that our system is not only more flexible than existing methods, but also, it makes a step towards satisfying desideratum (c). Specifically, it is significantly more accurate than all methods that do not require fine-tuning and even matches and outperforms the current state-of-the-art fine-tuning based methods on the most well-established benchmarks (PASCAL VOC & MSCOCO).
GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection

LiDAR and cameras are complementary sensors for 3D object detection in autonomous driving. However, it is challenging to explore the unnatural interaction between point clouds and images, and the critical factor is how to conduct feature alignment of heterogeneous modalities. Currently, many methods achieve feature alignment by projection calibration only, without considering the problem of coordinate conversion accuracy errors between sensors, leading to sub-optimal performance. In this paper, we present GraphAlign, a more accurate feature alignment strategy for 3D object detection by graph matching. Specifically, we fuse image features from a semantic segmentation encoder in the image branch and point cloud features from a 3D Sparse CNN in the LiDAR branch. To save computation, we construct the nearest neighbor relationship by calculating Euclidean distance within the subspaces that are divided into the point cloud features. Through the projection calibration between the image and point cloud, we project the nearest neighbors of point cloud features onto the image features. Then by matching the nearest neighbors with a single point cloud to multiple images, we search for a more appropriate feature alignment. In addition, we provide a self-attention module to enhance the weights of significant relations to fine-tune the feature alignment between heterogeneous modalities. Extensive experiments on nuScenes benchmark demonstrate the effectiveness and efficiency of our GraphAlign.