Reviewerly/exHarmony huggingface dataset:
Social and Ecological Impacts of Fire to Coastal Fisheries: A Study of the Kenai River Fishery (Alaska, <scp>USA</scp>)

Abstract In a rapidly changing environment where fires are becoming more frequent and severe, scientists and managers need information and tools to enhance understanding of the numerous ways in which fire can affect fisheries. We used Ostrom's social–ecological systems framework to structure the development and refinement of fuzzy cognitive maps with stakeholders across the Kenai River fishery in Alaska, USA. The process yielded a model characterizing the structure and function of the Kenai River fishery. The model was then used to guide interviews with stakeholders focused on the effects of the 2019 Swan Lake Fire. We identified seven direct pathways through which fire can affect the social and ecological components of a coastal fishery. We also used the model to guide a targeted literature review to further explore how fire can affect the components of the Kenai River fishery. This synthesis of information allowed us to develop a more complete understanding of the impacts of fire on the fishery—an understanding informed by input from local stakeholders (via our interviews) and relevant scientific and management literature (via our literature review). Lastly, we used the model to assess the fishery's vulnerability to fire. We discuss how early run Chinook Salmon Oncorhynchus tshawytscha constitute a highly vulnerable component of the system and have the greatest potential to impact the fishery as a whole through a chain of negative social and ecological interactions. This final step illustrates how the model can be used to assess the vulnerability of system components to specific threats. The model of the Kenai River fishery provides an empirically grounded and easily understood visual representation of the complex dynamics affecting coastal fisheries. As such, it can be used to structure discussions among managers and stakeholders, organize our understanding of how fire affects fisheries, and assess vulnerabilities to endogenous and exogenous stressors.
Discharge Prediction for Critical Patients Using Machine-Learning Technology

To predict four forms of discharge—death, home, nursing facility, and rehabilitation—machine learning models were developed. These models were trained on 115,248 distinct ICU admissions, and they were assessed. To lessen class disparity, we used synthetic minority oversampling techniques. Hierarchical and ensemble classifiers were used to examine the impact of an unequal testing set on the performance of our prediction models. With a 90% area under the receiver operating characteristic curve (recall 71%, F1: 70%), the XGBoost outperformed earlier tests in terms of discriminating performance. Our research demonstrates that the variables included in the proposed approach, known as the k-SMO (K-Sequential Minimal Optimization) model for assessing patient illness severity, are more accurate predictors of hospital release location than the current work score alone. Clinical decision support systems that incorporate these models may benefit patients, employees, and ICU staff by enabling them to begin disposition planning as soon as practical throughout their stay. The length of a hospital stay and its effects have a big effect on people and the economy. The present effort has focused on forecasting that crucial parameter as a consequence. Study groups have achieved successful prediction rates, although only very seldom revealing predictive patterns. The length of stay (LOS) forecasting model we present in this research, along with a study of trends and patterns that show how LOS differs amongst hospital departments and specialties, are also included. We also look into whether patient data might help hospital departments predict LOS more accurately. It is helpful to know where to concentrate attention while expecting LOS since the findings for these known patterns are often up to 21.61% better in terms of estimating error rates and up to 23.83% higher in terms of projecting success rates. Several trends appeared after the estimation of prediction rates. For women who were admitted to the emergency room for enteritis-related problems, for instance, these patterns improved prediction accuracy. Overall, the results show that the calculated error rates for these recognized patterns are up to 21.61% lower and the success rates for the number of predictions are up to 23.83% higher, which is useful for figuring out where to put the most effort when predicting LOS.
A FUSION OF HAND-CRAFTED FEATURES AND DEEP NEURAL NETWORK FOR INDOOR SCENE CLASSIFICATION

Convolutional neural networks (CNN) have proved to be the best choice left for image classification tasks. However, hand-crafted features cannot be ignored as these are the basic to conventional image processing. Hand-crafted features provide a priori information that often acts as the contemporary solution to CNN in image classification, and hence an attempt is made to fuse the two. This paper gives a feature fusion approach to combine CNN and hand-crafted features. The proposed methodology uses two stages, where the first stage comprises feature encoder that encodes non-normalized features of CNN, which utilizes edge, texture, and local features. The fusion of handcrafted features with CNN features is carried out in the second Hand-crafted crafted features are validated that helped CNN to perform better. Experimental results reveal that the proposed methodology improves over the original Efficient-Net(E) on the MIT-67 dataset and achieved an average accuracy of 93.87%. The results are compared with state-of-the-art methods.
Building VR Learning Material as Scaffolding for Design Students to Propose Home Appliances Shape Ideas

This study explores the development and evaluation of Virtual Reality in education using home appliance products as the example to develop VR teaching aids (Gravity Sketch) and teaching materials for testing and application through scaffolding teaching method. There are18 master students, who are familiar with the design process, and18 undergraduate students, who are unfamiliar with the computer aided design tools, from the Department of Industrial Design participate two experiments. The scaffold teaching method is employed in 2 semesters to construct and execute the theory respectively. This study transforms the VR learning process of the MA students into a supplementary teaching aid to guide the second year students, and explores the students’ nearside development area through VR sketch modelling and Alias modelling tasks. The scaffold was removed after the learning progress, and the students continues to work on various modelling details (e.g. R-angles and transition surfaces) in the Alias course. After the four-week VR course, most of the students were able to submit three different kinds of product shapes in Alias and were significantly more confident in mastering and manipulating 3D spatial sense and product shape modify ability. VR is a highly effective tool between hand drawing and CAD, it can help design students to express their shape ideas in an intuitive and freedom way, to overcome the frustrating modelling barriers of using 3D software.
The development of an Empathetic Chatbot for Depression Management

본 논문은 우울 증상을 가진 사람들을 위한 공감형 챗봇 개발에 관한 연구이다. 현대 사회의 많은 사람이 우울 장애와 같은 정신 질환으로 고통받고 있지만, 현실적으로 정신건강의학과 도움을 받는 데 한계가 있다. 따라서 우울한 감정을 호소하는 사람들이 시간과 공간의 제약을 받지 않고, 챗봇을 통해 우울한 감정을 약화하는 것을 목표로 한다. 공감형 챗봇은 검색 기반 모델과 생성 모델의 결합 구조를 띤다. 먼저, Poly Encoder를 통해 저장된 데이터베이스 내에서 최적의 후보군을 도출한다. 이후 Cross Encoder를 통해 후보군 재 순위화 과정을 거쳐 최종 답변을 선택한다. 만약 해당 후보군이 최종 답변으로 적절하지 않을 경우, 생성 모델을 통해 새로운 답변 문장을 생성한다. 위 구조를 통해 각 모델이 갖는 한계점을 극복하여 대화가 잘 이루어짐과 동시에 공감형 답변을 하는 챗봇 실현이 가능하다. This paper proposes a novel solution to the problem of limited access to mental health resources for individuals experiencing depressive symptoms. We propose the development of an empathetic chatbot that can provide support and reduce depression regardless of time and space constraints. Our chatbot utilizes a combination of retrieval-based and generative models. First, the Poly-encoder extracts the most relevant candidates from the stored database. Then, the Cross Encoder re-ranks the extracted candidates to identify the most appropriate answer for users. In cases where the final candidate is unsuitable, the generative model generates a new response that aligns with the user’s emotions. This approach allows us to overcome the limitations of each model and create a chatbot capable of delivering empathetic and supportive answers. Our ultimate goal is to offer a practical and accessible solution to support those struggling with depressive disorders.
Effects of mobile-assisted reading materials on children’s L1 lexical development

Despite the wide and extensive use of mobile-assisted devices, the effectiveness of children's L1 learning with these mobile-assisted technologies has been less discussed. This study aims to explore the effects of mobile-assisted reading materials on Chinese children's L1 vocabulary learning. We adopted a longitudinal and quasi-experimental design consisting of an experiment group using the mobile-assisted materials and a control group using the traditional paper materials, and took children's lexical development as indexed by assessing the parameter, lexical diversity, in different testing times. The results showed that (1) children's L1 vocabulary learning effectiveness of using mobile-assisted materials is as similar as that of using conventional paper materials in general, and (2) the changing patterns of children's L1 lexical development using mobile-assisted materials in different testing times are various. Specifically speaking, (a) in the posttest 1 (the first month), compared with the traditional paper reading materials, the mobile-assisted reading materials have a facilitating effect on the primary school students' L1 vocabulary learning; (b) in the posttest 2 (the second month), children's vocabulary learning effectiveness is inhibited by the mobile-assisted reading materials; (c) in the delayed posttest (the fourth month), there is no difference in the learning effectiveness by these two different kinds of learning materials and the lexical diversity increases slowly but steadily. We analyzed the results from research-design factors and learner-related factors, hoping to shed light on children's mobile-assisted language learning research.
SE‐Former: Incorporating sentence embeddings into Transformer for low‐resource NMT

Abstract Recently, pre‐trained language models (PLM), such as Roberta and SimCSE, demonstrate strengths in many natural language understanding (NLU) tasks. However, there are few works on applying PLM to neural machine translation (NMT). Motivated by alleviating the data scarcity issue of low‐resource NMT, here incorporating sentence embeddings from SimCSE into the Transformer network is explored and SE‐Former model is proposed. In this model an embed‐fusion module is designed to utilize the output of SimCSE for NMT. Specifically, the outputs of encoder and SimCSE are fed into embed‐fusion module, attention network learns the relationship of sentence embedding and corresponding word embeddings. After addition, concatenation and linear transformation operations, the tensor fused with sentence embedding is obtained. The size of tensor output by embed‐fusion module is the same as original encoder. Finally, embed‐fusion module is connected to Transformer decoder. On IWSLT En‐Es, En‐Zh and En‐Fr tasks, SE‐Former obtains 42.13, 29.32 and 39.21 bilingual evaluation understudy (BLEU) points, respectively. Experimental results show the superiority of this method.
Solution Separation-based Integrity Monitor for Integrated GNSS/IMU/Camera Navigation: Constraining the Hypothesis Space With Deep Learning

This article introduces a method utilizing deep learning in typical Multiple Hypothesis Solution Separation (MHSS)-based Integrity Monitors (IMs) of autonomous vehicle navigation systems, when conventional sensors, such as GNSS and Inertial Measurement Unit (IMU), are integrated with a camera. It is an innovative methodology to reduce the hypothesis space of sensor faults. In the proposed method, the measurement subsets to evaluate in MHSS are generated from the IMU/GNSS measurement set only, so that Fault Detection and Exclusion (FDE) in camera measurements takes place separately. In the investigated approach, anomaly prediction in state estimate error due to camera faults is performed based on raw images with a Deep Neural Network (DNN), and IM input is required only during the online refinement of predicted anomaly locations to reflect anomalies in the IM test statistic. This opens for the possibility to evaluate environment features and conditions that cause specific detected or undetected sensor faults. Experiments on the IMU/GNSS/Camera integration demonstrated that Protection Level (PL) bounding performance of the proposed IM, with limited hypothesis space and the individual camera FDE, is comparable to the MHSS IM informed by the full set of fault hypotheses. Despite the use case of the camera, the method can be directly extended to integrations with multiple auxiliary sensors, where each auxiliary sensor is evaluated individually for faults.
Progress of Science

A study of the relationship of trends towards specialization and universalization of scientific knowledge is most fruitful when sociological and epistemological methods are compared. Sociological methods describe the growth of scientific knowledge quantitatively as an increase in volume, whereas epistemological methods do it qualitatively in terms of an increase in the level of generalization. The sociological explanation of the specialization of researchers is based on the limited resources of the study time, the epistemological explanation of the differentiation of disciplines is in the ontological differences of the objects of research. On the contrary, epistemological universalization – the formulation of generalizing theories is conditioned by the social need to ensure the connectivity of the network of technical practices. The history of the development of natural sciences shows a tendency towards absorption of the less developed paradigms by more developed ones. In other words, scientific knowledge increases cumulatively both in quantitative and qualitative terms.
A Novel Methodology for Credit Card Fraud Detection using KNN Dependent Machine Learning Methodology

Credit cards offer a convenient and efficient option for online transactions; however, their increasing use has led to a rise in credit card fraud, resulting in significant financial losses for both cardholders and financial institutions. This research aims to identify such frauds by considering various criteria, including the availability of public data, high-class disparity statistics, changes in fraudulent processes, and high false alarm rates. With the growth of e-payments, fraudsters have resorted to various tactics such as fake emails and data breaches to steal money during online transactions. Although these methods are inaccurate, cutting-edge machine-learning algorithms must be used to reduce fraud losses. Therefore, this study's primary focus is on the recent advancements in machine learning algorithms for credit card fraud detection. The research paper aims to investigate the application of machine learning algorithms in distinguishing between genuine and fake online transactions. In the paper, KNN is compared to other machine-learning methods for detecting credit card fraud. The proposed approach achieved an accuracy of 99.95%, a precision of 97.2%, a recall of 85.71%, and an F1-score of 90.3%.
Pengaruh Pola Asuh Orang Tua terhadap Motivasi Belajar Siswa di Kelas V UPTD SDN 124394 Pematang Siantar

This study aims to determine the effect of parenting style on the learning motivation of fifth grade students at SD Negeri 124394 Pematang Siantar in the 2022/2023 academic year. In this study, researchers used a type of quantitative research. The population in this study were all fifth grade students, totaling 50 people using the entire population as the research sample. The process of collecting data using observation instruments, questionnaires and documentation, by using data analysis techniques instrument test, prerequisite test, descriptive statistics and hypothesis testing. The results of this study indicate that the value of tcount ˃ ttable is 3.627 ˃ 1.056 and with a significance value of 0.002 ˂ 0.05 and produces an R Square of 0.409, so it is concluded that social interaction has an effect of 40.9% on thematic learning outcomes of class IV students at SDN 124386 Pematang Siantar.
Information Security Protection Techniques for Substation Operation and Maintenance Edge Gateway

The cloud-edge collaborative operation and maintenance system based on edge computing realizes the remote operation and maintenance of substation equipment. As the core equipment of the system, the edge gateway is the carrier of local processing of operation and maintenance business and cloud side collaboration. Its intrinsic safety is directly related to the reliability of the operation and maintenance system. This paper designs a complete information security protection scheme for substation edge gateway from the aspects of equipment ontology, microservice technology, communication authentication, identity authentication, security audit, business process and so on. Through network attack, vulnerability scanning, integrity verification and other security testing methods, the feasibility of the scheme is verified. This scheme can effectively improve the security level of the operation and maintenance system while ensuring the main business performance of the system.
A semi supervised approach to Arabic aspect category detection using Bert and teacher-student model

Aspect-based sentiment analysis tasks are well researched in English. However, we find such research lacking in the context of the Arabic language, especially with reference to aspect category detection. Most of this research is focusing on supervised machine learning methods that require the use of large, labeled datasets. Therefore, the aim of this research is to implement a semi-supervised self-training approach which utilizes a noisy student framework to enhance the capability of a deep learning model, AraBERT v02. The objective is to perform aspect category detection on both the SemEval 2016 hotel review dataset and the Hotel Arabic-Reviews Dataset (HARD) 2016. The four-step framework firstly entails developing a teacher model that is trained on the aspect categories of the SemEval 2016 labeled dataset. Secondly, it generates pseudo labels for the unlabeled HARD dataset based on the teacher model. Thirdly, it creates a noisy student model that is trained on the combined datasets (∼1 million sentences). The aim is to minimize the combined cross entropy loss. Fourthly, an ensembling of both teacher and student models is carried out to enhance the performance of AraBERT. Findings indicate that the ensembled teacher-student model demonstrates a 0.3% improvement in its micro F1 over the initial noisy student implementation, both in predicting the Aspect Categories in the combined datasets. However, it has achieved a 1% increase over the micro F1 of the teacher model. These results outperform both baselines and other deep learning models discussed in the related literature.
Modeling Processes of Emergence and Growth of Fractal Structures and Chamber Defects of Thermal Power Units

The paper considers modeling the emergence and growth of possible fractal structures and defects on the inner surface of the chambers of thermal power units, which allow control and regulation of the development of such stochastic dynamic processes. We are talking about the use of modeling procedures for the given analysis, which are quite universal in the framework of certain approaches. A general qualitative consideration of the modeling of processes such as the deposition of materials on a solid surface of various complex configurations, and the occurrence of its fracturing and defects of fractal types, is given. In the first part, we consider an analogy between the processes of the emergence and growth of fractal structures and chamber defects with laser processes of controlled deposition of substances on the surface of a solid body and the growth of its fracturing. A number of models have been developed for the emergence and growth of fractal inhomogeneities of various types and configurations on a solid surface with identification of the possibility of their preliminary monitoring at the initial stage of their appearance. At the same time, both various surfaces in the form of coatings and the formation of fracturing and cluster zones of inhomogeneities and defects have been analyzed. The emphasis in the course of research has been made both on the analysis of their regulated structure, and also on the dynamics of the growth of fracturing and cluster zones of heterogeneities and defects in a certain direction. At the initial stage, the indicated process of formation of fractures and cluster zones of inhomogeneities and defects in space was monitored with different scaling: nano- and microscales. This study has been carried out within the framework of an analogy under the following conditions: firstly, given control over the cha-racteristics of similar objects in the corresponding laser experiment; secondly, the presence of a real possibility of ensuring their formation on the inner surfaces of the chambers in contact with the working substance; thirdly, the possibility of such structures influencing on the efficiency of various types of thermal power units in the context of achieving the desired direction and/or undesirability of changing the indicators and characteristics of units taking into account the laws of technical thermodynamics.
Quantum Cryptography Simulation of Entanglement and Quantum Teleportation

The objective of this article is to present a simulation of quantum information. We take advantage of quantum entanglement to ensure the spontaneous transfer of a state from one qubit to another similar qubit. Similarly, the principle of quantum teleportation is conceived in the application; it is not about transfer of matter, but of information. Indeed, two systems already exist, and at the end of the teleportation, the process is destructive—the first system will no longer be in the same state as the initial state and the second system takes the state of the first, which does not contradict the no-cloning theorem.
Wav2vec‐MoE: An unsupervised pre‐training and adaptation method for multi‐accent ASR

Abstract In real life, either the subjective factors of speakers or the objective environment degrades the performance of automatic speech recognition (ASR). This study focuses on one of the subjective factors, accented speech, and attempts to build a multi‐accent ASR system to solve the degradation caused by different accents, one of whose characteristic is the low resource. To deal with the challenge of the low‐resource data and the different speech styles, a wav2vec‐MoE (mixture of experts) is proposed to adapt the wav2vec 2.0 for multi‐accent ASR. In the wav2vec‐MoE, a domain MoE is developed by introducing pseudo‐domain information in the pre‐training stage, where the domain denotes a collection of speech varied by the same influence factors. The MoE is trained with two strategies according to the proposed domain mismatch assessment between unlabeled speech and target speech, without requiring any explicit domain information. Experiments show that the wav2vec‐MoE achieves a 14.69% relative word error rate reduction (WERR) on the AESRC2020 accent dataset and an 8.79% relative WERR on the Common Voice English dataset.