Reviewerly/exHarmony huggingface dataset:
Exploring Predictive Factors of Dropout Rate of 4-year Universities using Machine Learning

The purpose of this article was to identify factors that predict the dropout rate in 4-year universities using machine learning algorithms. The Random Forest and XGBoost algorithms were employed, and data from the Korean Council for University Education and the University Information Disclosure System were utilized for model development. The collected data spanned from 2011 to 2021 and included a total of 17 independent variables related to educational finance, educational conditions, and educational and research performance. The dropout rate was analyzed as the dependent variable. The final Random Forest and XGBoost models demonstrated the ability to explain 65% and 66% of the variability in the dropout rate, respectively. Using the two machine learning models, the top 10 explanatory variables were identified based on their importance, excluding the student enrollment rate. The models identified the following variables as important predictors of the dropout rate: expenditure per student for academic materials, research budget per full-time faculty member within the university, research budget per full-time faculty member outside the university, freshman enrollment rate, employee-to-student ratio, and employment rate. By examining the partial dependence plots of the five common important explanatory variables, it was analyzed how the predicted dropout rate changes in response to variations in these variables. Although there were differences in the curves between Random Forest and XGBoost, the overall direction and trends of the curves for the important explanatory variables were found to be similar. The improvement of these five important variables was found to have a negative correlation with the dropout rate, indicating a decrease in the predicted dropout rate. Based on these research findings, key implications and suggestions for further studies were provided.
Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women

The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.
A Deep Learning Based Methodological Analysis for Breast Cancer Classification

Breast cancer (BC) is the world's most common and rapidly spreading disease.BC can be controlled, reducing the death rate if detected early.As a result, some researchers have recently developed deep-learning-based efficient algorithms for predicting cancer cell growth using different modalities of medical imaging, for example, mammograms, tomosynthesis, MRI ultrasound, etc., for their efficiency and accuracy.Only a few review articles on BC diagnosis synthesize some existing studies.These investigations, unfortunately, are unable to identify new structures and modalities in the diagnosis of BC.The changing frameworks of DL for BC detection are the subject of this review.This assessment explores the strengths and limitations of earlier deep-learning (DL) based methods, investigates the datasets employed, and examines image preprocessing approaches based on different medical imaging modalities.The performance results with evaluation, obstacles, and further enhancement are also provided.
Real-time detection of a camouflaged object in unstructured scenarios based on hierarchical aggregated attention lightweight network

Accurate and real-time grasping of household items is a challenge for home service robots in complex indoor environments. In this paper, using transparent cups as the recognition object, an accurate, reliable, and real-time visual algorithm is proposed for the elderly who needs nursing care to solve the recognition problems of overlapping, blurred, and small cups in complex unstructured indoor scenes. In order to meet the lightweight deployment of home service robots while considering both high-scale and low-scale semantic features, this paper designs a multi-scale fusion lightweight backbone network based on the Split Shuffle Block (SSB) and Group Shuffle Block (GSB) feature encoding units. Among them, depthwise separable convolution (DwConv) uses to reduce the amount of coding unit parameters, and feature shuffle uses to promote information exchange and the ability of information expression. In order to accurately identify the transparent cup camouflaged in complex backgrounds, this paper proposed a lightweight feature enhancement module that combines multi-scale hierarchical aggregation attention and multi-branch parallel convolution structure. The proposed module used an adaptive weighting strategy and a channel normalization weighting strategy to highlight the active regions of boundary features in each branch feature map, enhance the exchange of boundary information, and reduce the loss of detailed information. The experimental results on the IndoorCup and MobileCup datasets show that the detection accuracy of the proposed method is 93.6% and 92.6%, respectively, and the model calculation amount is only 0.91 M, which can lightweight deploy on indoor mobile robots for real-time detection. From the results of qualitative comparisons, the proposed method has strong robustness. It can effectively suppress false and missed detection caused by background interference. Likewise, it also effectively identifies camouflage objects and small objects in complex backgrounds.
Applying anti-racist approaches to informatics: a new lens on traditional frames

Abstract Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on (1) critical self-reflection, (2) following the expertise of well-established scholars of racism, (3) centering the voices of affected individuals and communities, and (4) critically evaluating practice resulting from informatics systems, stakeholders can work to minimize the impacts of racism. Informatics, informed and guided by this proposed framework, will help realize the vision of health systems that are more fair, just, and equitable.
Embedded system architecture-computer embedded software defect prediction based on genetic optimisation algorithms

With the rapid development of electronic measurement technology, people have put forward higher requirements for the diversity of oscilloscope functions and abundant peripheral interfaces. This paper aims to use genetic optimisation algorithms to detect embedded software defects, provide users with prepared defect information, and improve the efficiency and accuracy of detection. This paper proposes popular algorithms for moving target video detection, selection operator, crossover operator and mutation operator, and establishes a complete system, deepening a virtual simulation environment for embedded software development model. In addition, from hardware simulation to the detection of software defects such as memory leaks and uninitialised variables, they are all included in the system and run through the entire process of embedded software development. The experimental results in this paper show that the complete simulation technology has realised a multi-architecture emulator Emu, combined with the defect detection software Valgrind, has realised a complete lack of phase detection system, and the detection rate is as high as 96.7%.
Development of Communication Skills of Second-Graders: Features of the Implementation of the Requirements of the Federal State Educational Standard of Primary General Education

The article analyzes the difficulties in implementing the updated versions of the Federal State Educational Standard for Primary General Education and the Federal Educational Program for Primary General Education. The problem of developing the communicative skills of second-graders is considered, the ways of teaching younger students to communicate in the dialogue mode are updated. As a priority, it is proposed to use forms of joint activities that meet the requirements of the updated program documents. It is noted the need for the gradual formation in children of the ability to listen to the interlocutor and perceive information to complete educational tasks. Tasks for second-graders are offered, which help students understand the importance of mastering communication skills in the process of studying the Russian language course.
Advances towards a better prediction of weather extremes in the Destination Earth initiative 

Destination Earth &amp;#8211; DestinE &amp;#8211; is an ambitious initiative of the European Commission, in support of its Digital Strategy and the Green Deal. Bringing together scientific and industrial excellence from across Europe, DestinE will contribute to revolutionising the European capability to monitor and predict our changing planet, complementing existing national (meteorological services) and European efforts (Copernicus services). Based on the integration of extreme-scale computing, Earth system simulations, and the real-time exploitation of all available environmental observations, and machine learning techniques, DestinE will develop high-accuracy digital twins, or replicas, of the Earth.&amp;#160; DestinE will thus allow users to better explore the evolution of the Earth system, and to test a range of scenarios and potential mitigation strategies. Under the European Commission's leadership, and in coordination with the Member States, scientific communities and other stakeholders, ECMWF, ESA and EUMETSAT are the three entrusted entities tasked with delivering the first phase of the DestinE by 2024. ECMWF is responsible for building the &amp;#8216;digital twin engine&amp;#8217; software and data infrastructure and for using it to deliver the first two high-priority digital twins, while&amp;#160;European Space Agency&amp;#160;(ESA) provides the platform through which users will access the service, and the&amp;#160;European Organisation for the Exploitation of Meteorological Satellites&amp;#160;(EUMETSAT) develops the data repository. This talk will give a high-level introduction to the initiative, and it will particularly focus on one of the first two priority digital twins that ECMWF is developing: The Digital Twin on Weather-Induced and Geophysical Extremes (Extremes DT). This Digital Twin will provide capabilities and services for the assessment and prediction of environmental extremes from global to local scale, on a time scale of a few days ahead. In this talk, we will particularly focus on the global, continuous component of the Extremes DT, that we are developing at ECMWF using our Integrated Forecasting System. We will showcase results from simulations at 4.5km and 2.8km resolution, to illustrate the benefits of the resolution increase for the representation of extreme events such as tropical cyclones, medicanes or intense precipitation events in mountainous regions. We will also discuss the lessons learned regarding aspects of the forecasting system that need to be further improved to exploit the advantages of the resolution increase for better representing extreme events.
Preserving Security of Crypto Transactions with Machine Learning Methodologies

In recent years, with the increasing popularity of cryptocurrencies, the number of fraudulent activities in the crypto space has also risen. To combat this, machine learning techniques have been developed for detecting fraudulent transactions. This study, compares the performance of three popular machine learning algorithms, Logistic Regression, Random Forest Classifier, and XGB Classifier, for detecting crypto fraud. And they utilize a dataset containing features related to the transaction, such as the time of the transaction, the amount involved, and the country of origin. The results show that all three algorithms achieve high accuracy in detecting fraud, with XGB Classifier outperforming the other two. These results show that all three algorithms are capable of detecting fraudulent activities with high accuracy, with XGB Classifier outperforming the other two. This study provides valuable insights into the use of machine learning in detecting fraudulent activities within the cryptocurrency industry and highlights the potential of these algorithms for future research in this area.
Architecture-Aware Optimization of Layer Fusion for Latency-Optimal CNN Inference

Layer fusion is an effective technique for accelerating latency-sensitive CNN inference tasks on resource-constrained accelerators that exploit distributed on-chip integrated memory-accelerator processing-in memory (PIM). However, previous research primarily focused on optimizing memory access, neglecting the significant impact of hardware architecture on latency. This study presents an analytical latency model for a 2D systolic array accelerator, taking into account various hardware factors such as array dimensions, buffer size, and bandwidth. We then investigate the influence of hardware architecture and fusion strategies, including weight and overlap reuse, on performance; these aspects are insufficiently addressed in existing access-based fusion models. By incorporating layer fusion with our proposed latency model across different architectures, dataflows, and workloads, we achieve up to a 53.1% reduction in end-to-end network latency compared to an access-based model.
Motion Planning and Calculation Method of Power Inspection Robot Arm

Aiming at the implementation of high-accuracy path planning for power inspection robots in complex environments, a virtual model of the operating robotic arm is established. In this paper, a series of collision-free waypoints are obtained while using the grid-based C-space method to establish the movement space of the mechanical arm and map the obstacles. On the premise of these waypoints, the interpolation method with arc transition curves is used to fit the motion trajectory smoothly. Finally, an example is given to verify the effectiveness of the proposed method.
Semantic Disentanglement Adversarial Hashing for Cross-Modal Retrieval

Cross-modal hashing has gained considerable attention in cross-modal retrieval due to its low storage cost and prominent computational efficiency. However, preserving more semantic information in the compact hash codes to bridge the modality gap still remains challenging. Most existing methods unconsciously neglect the influence of modality-private information on semantic embedding discrimination, leading to unsatisfactory retrieval performance. In this paper, we propose a novel deep cross-modal hashing method, called Semantic Disentanglement Adversarial Hashing (SDAH), to tackle these challenges for cross-modal retrieval. Specifically, SDAH is designed to decouple the original features of each modality into modality-common features with semantic information and modality-private features with disturbing information. After the preliminary decoupling, the modality-private features are shuffled and treated as positive interactions to enhance the learning of modality-common features, which can significantly boost the discriminative and robustness of semantic embeddings. Moreover, the variational information bottleneck is introduced in the hash feature learning process, which can avoid the loss of a large amount of semantic information caused by the high-dimensional feature compression. Finally, the discriminative and compact hash codes can be computed directly from the hash features. A large number of comparative and ablation experiments show that SDAH achieves superior performance than other state-of-the-art methods.
A Process for Identifying and Modeling Relevant System Context for the Reconfiguration of Automated Systems

Context-aware systems are gaining increasing importance in industry as managing and using context information opens new opportunities for reconfiguration, digital twin concepts, performance monitoring, and so forth. Today, there is an immense variety of context-aware systems, from mobile applications to complex systems which require run time reconfiguration. The diversity of methods for acquiring the relevant data, as well as the specific use case requirements, complicate the management and usage of context information. Thus, the usage of context information for the reconfiguration management of systems demands adequate processes to identify and model system-relevant context. Consequently, in this research, we present a new method for identifying and modeling the context information that is relevant to trigger a system reconfiguration as well as automated context model creation in the form of ontologies and feature models. We suggest the ontology usage to disambiguate the data from different sources. The implementation of context-aware behavior is beyond the scope of this article and will be included in future research. We validate our ideas using cases study in the wind energy domain and unmanned automated vehicle domain. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Dynamically changing environments, require systems that continuously monitor their environment and adapt when necessary to ensure safe and optimal operation. For the system to adapt, it is necessary to define which of the environmental variables are relevant and when a change should lead to a reconfiguration of the system. The method presented in this article helps practitioners to identify these environmental variables. A rapidly changing environment also means having to interact with different systems from different vendors. The relevant environmental variables could be provided by different systems or collected by different sensors that rely on different data models. To cope with this heterogeneous environment, it is useful to create a formalized and reusable context model, describing the relevant environmental variables. A formalized context model serves as a semantic layer to link heterogeneous data and enables operation in a changing environment. In this way, the result of the presented method is transformed automatically into an ontology.
Development of Autonomous Vehicle Traffic Accident Scenarios in Urban Areas Based on Real-World Accident Data Using Association Rule Mining

자율주행차 개발에 따라 교통사고 감소 등이 기대되고 있으나, 자율주행차 사고가 끊임없이 발생하고 있어 안전성 및 신뢰성 문제가 대두되고 있다. 이를 해소하기 위해 자율주행차 안전성 평가 시나리오가 활용되고 있으며, 이는 차량의 안전과 직결되어 있어 시나리오의 신뢰성 및 구체성이 필수적으로 요구된다. 이에 본 연구는 자율주행차 안전성 평가를 위해 신뢰성과 구체성을 확보한 자율주행차의 도심부 사고 시나리오 생성 방법론을 제안하였다. 시나리오의 신뢰성을 확보하기 위해 자율주행차 실사고 데이터인 DMV collision report를 활용하였으며, 구체성을 확보하기 위해 데이터 마이닝 기법인 연관규칙 분석의 Apriori 알고리즘을 활용하였다. DMV collision report를 활용하여 연관규칙을 분석한 결과, 약 300,000개의 연관규칙이 확인되었으며, 차량 충돌 형태와 연관성이 높은 다양한 사고 요인의 조합을 도출하였다. 구체적인 시나리오를 제시하기 위해 PEGASUS 프로젝트와 CETRAN 프로젝트를 분석하여 최종적으로 9개의 시나리오 요인을 선별하였으며, 해당 시나리오 요인을 모두 포함한 연관규칙을 기반으로 6개의 자율주행차 도심부 사고 시나리오를 제시하였다. 본 연구에서 제시한 6개의 시나리오는 자율주행차의 안전성 평가를 위한 시나리오로 활용 가능할 것으로 기대된다. Although traffic accidents are expected to decrease with the development of autonomous vehicles, safety and reliability issues are emerging as traffic accidents with autonomous vehicles are constantly occurring. To address this issue, an autonomous vehicle safety evaluation scenario is utilized that is directly linked to vehicle safety. Therefore, ensuring the reliability and specificity of the scenario is crucial. Accordingly, we proposed a methodology for creating a reliable and specific autonomous vehicle accident scenario in urban areas to evaluate the safety of autonomous vehicles. To ensure the reliability of the scenario, we utilized the DMV collision report, which contains autonomous vehicle accident data. Additionally, to improve specificity, we employed the Apriori algorithm of association rule analysis, a data mining technique. After analyzing the association rules using the DMV collision report, we identified approximately 300,000 association rules and derived a combination of various accident factors that were highly related to the type of vehicle collision. To provide specific scenarios for evaluating the safety of autonomous vehicles, we analyzed the PEGASUS project and the CETRAN project and finally selected nine scenario factors. Based on the association rules that included all of the scenario factors, we presented six autonomous vehicle traffic accident scenarios in the urban areas. These scenarios are expected to be utilized as a standardized benchmark for evaluating the safety of autonomous vehicles.
An Improved Seagull Algorithm for Numerical Optimization Problem

In Artificial Intelligence, numerical optimization is an instantly rising research domain. Swarm Intelligence (SI) and Evolutionary Algorithm (EA) are widely used to answer the problems where the optimal solution is required. Inspired by Seagull’s natural behavior, the Seagull Optimization Algorithm (SOA) is a meta-heuristic, swarm-based intelligent search method. SOA algorithm is a population-based intelligent stochastic search procedure that inherited the manner of seagulls to seek food. In SOA, population initialization is crucial for making rapid progress in a d-dimensional search space. In order to address the issue of premature convergence, this research presents a new variation called the Adaptive Seagull Optimization Algorithm (ASOA). Second, a variety of starting methods have been suggested as ways to enhance seagulls’ propensity for exploratory activity. To improve the diversity and convergence factors, instead of applying the random distribution for the initialization of the population, Qusai-random sequences are used. This paper reveals the state-of-the-art population initialization, and a new SOA variant is introduced using adaptive mutation strategies to prevent local optima. To simulate and validate the results of ASOA and initialization techniques, 8 different benchmark test functions are applied; some are uni-modal, and some are multimodal. The simulation results depict that proposed variant ASOA provides superior results.