Reviewerly/exHarmony huggingface dataset:
Nearly Heisenberg-limited noise-unbiased frequency estimation by tailored sensor design

The authors consider frequency estimation in the presence of spatiotemporally correlated noise. They show that the usual approach involving unbiased estimators is ill-suited in such cases, and introduce new estimators which, at the cost of doubling the required resources, are insensitive to noise and retain the precision scaling of standard ones.
Time-Awareness in Object Exploration Tools: Toward In Situ Omniscient Debugging

Exploration of state and behavior is essential for understanding and debugging object-oriented programs. Many time-related questions about object communication – an object’s history – only arise in the context of a specific error in the here and now. At such a specific point in time, however, it is often distracting to involve omniscient debugging tools such as program tracers, because they do not integrate well with the programmer’s current focus on space-related questions and the informational cues at hand. In this paper, we present a novel way to provide a tangible, consolidated notion of both space and time in object exploration tools to make it more likely that programmers will use the available means to explore the evolution of particular objects. With programmers remaining informed about and in control of a program’s space and time, we promote the scientific method for debugging and leverage exploratory programming practices. We evaluate our model with hands-on experiences in the Squeak/Smalltalk programming system, using a program tracer that we have integrated into existing exploration tools to promote both spatial and temporal views. We believe that a clear, tangible notion of spacetime can help tool designers provide a better programming experience for those constantly recurring “What happened to this object?” situations.
Towards an Industrial Stateful Software Rejuvenation Toolchain using Model Learning

We present our vision for creating an industrial legacy software rejuvenation toolchain. The goal is to semi automatically remove code smells from stateful software used in Cyber Physical Systems (CPS). Compared to existing tools that remove code smells, our toolchain can remove more than one type of code smell. Additionally, our approach supports multiple programming languages because we use abstract models obtained by means of model learning. Supporting more than one programming language is often lacking in state of art refactoring tools.
Adiabatic quantum learning

Quantum machine learning has attracted considerable interest due to its potential to improve certain learning tasks. In conventional quantum machine learning, the output is the expectation value of a preselected observable, and the projective measurement forces a quantum circuit to run many times to obtain the output with reasonable precision. In this work, we propose a protocol to utilize the adiabatic quantum evolution to execute quantum learning tasks, in which the output is obtained by the adiabatic weak measurement rather than the projective measurement. In comparison to previous protocols, we use only a single-shot measurement and therefore avoid the measurement repetition in the previous protocols. Moreover, our protocol allows us to extract the expectation values of multiple observables without disrupting the concerned quantum states.
Verifiable proofs for the energy supply chain: small proofs brings you a long way

Abstract We describe a solution for secure and verifiable handling of energy certificates. Such certificates are increasingly used to claim and prove responsible use of green energy, and there is a strong need for transparency and public verifiability. While the proposed solution is designed for handling electricity it applies to different types of energy as well and the concepts may also be applied to other domains. Transmission System Operators are trusted to record consumption and production of electricity. The movement from volume-based MWh yearly certificates to spot-market aligned hourly or 15 min time-volume based intervals, creates challenges in relation to handling large amounts of data and subsequent transactions. Small discrete intervals gives the certification increased accuracy of energy consumption, as a means to prevent greenwashing, with the cost of higher amounts of transactional data and complexity. To ensure trust in the certification, these certificates must in addition be unique and publicly verifiable. This paper describes how blockchain technology can be used to create the required transparency and public verifiability. We show how large amounts of data can be efficiently handled on blockchains and how confidential data such as the amount of used energy in the certificates can be protected, ensuring privacy and correctness of the certificates.
A Task-Risk Consistency Object Detection Framework Based on Deep Reinforcement Learning

A discernible gap has materialized between the expectations for object detection tasks in optical remote sensing images and the increasingly sophisticated design methods. The flexibility of deep learning object detection algorithms allows the selection and combination of multiple basic structures and model sizes, but this selection process relies heavily on human experience and lacks reliability when faced with special scenarios or extreme data distribution. To address these inherent challenges, this study proposes an approach that leverages deep reinforcement learning within the framework of vision tasks. This study introduces a Task-Risk Consistent Intelligent Detection Framework (TRC-ODF) for object detection in optical remote sensing images. The proposed framework designs a model optimization strategy based on deep reinforcement learning that systematically integrates the available information from images and vision processes. The core of the reinforcement learning agent is the proposed task-risk consistency reward mechanism, which is the driving force behind the optimal prediction allocation in the decision-making process. To verify the effectiveness of the proposed framework, multiple sets of empirical evaluations are conducted on representative optical remote sensing image datasets: RSOD, NWPU VHR-10, and DIOR. When applying the proposed framework to representative advanced detection models, the mean average precision (mAP@0.5 and mAP@0.5:0.95) is improved by 0.8–5.4 and 0.4–2.7, respectively. The obtained results showcase the considerable promise and potential of the TRC-ODF framework to address the challenges associated with object detection in optical remote sensing images.
A taxonomy and review of generalization research in NLP

Abstract The ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a taxonomy for characterizing and understanding generalization research in NLP. The proposed taxonomy is based on an extensive literature review and contains five axes along which generalization studies can differ: their main motivation, the type of generalization they aim to solve, the type of data shift they consider, the source by which this data shift originated, and the locus of the shift within the NLP modelling pipeline. We use our taxonomy to classify over 700 experiments, and we use the results to present an in-depth analysis that maps out the current state of generalization research in NLP and make recommendations for which areas deserve attention in the future.
Feature Relation Guided Cross-View Image Based Geo-Localization

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type.
A RAW Image Noise Suppression Method Based on BlockwiseUNet

Given the challenges encountered by industrial cameras, such as the randomness of sensor components, scattering, and polarization caused by optical defects, environmental factors, and other variables, the resulting noise hinders image recognition and leads to errors in subsequent image processing. In this study, we propose a RAW image denoising method based on BlockwiseUNet. By enabling local feature extraction and fusion, this approach enhances the network’s capability to capture and suppress noise across multiple scales. We conducted extensive experiments on the SIDD benchmark (Smartphone Image Denoising Dataset), and the PSNR/SSIM value reached 51.25/0.992, which exceeds the current mainstream denoising methods. Additionally, our method demonstrates robustness to different noise levels and exhibits good generalization performance across various datasets. Furthermore, our proposed approach also exhibits certain advantages on the DND benchmark(Darmstadt Noise Dataset).
THE EFFECT OF OUTDOOR INQUIRY PROGRAM FOR LEARNING BIOLOGY USING DIGITAL TWIN TECHNOLOGY

The outdoor inquiry has a significant meaning in learning biology, but it has a problem that is difficult to be frequently implemented in the school garden due to causes. On the other hand, alternative activities using the virtual world have been proposed, but due to a lack of reality and passive use, the continuity of the activities is low, and there are doubts about the effect on the affective domain. Therefore, this study developed a class program in which students directly construct a virtual world and explore living organisms using the digital twin platform. Also, researchers checked the changes in students' affective domain according to the application of the learning program. A teaching and learning strategy for learning biology was composed through the review of research and statistical analysis performed changes of the affective domain. The experimental group changed more positively than the control group in the affective domain of learning biology due to replicating the school garden so that living organisms can be explored indoors and outdoors. Consequently, class programs for learning biology can positively affect the learner's affective domain when it is provided with improved realism by digital twin, self-directedness, and autonomy to compare real space and object. Keywords: affective domain, digital twin, learning biology, outdoor inquiry, school garden, virtual world
Fractal dimension and Quark–Gluon plasma

The properties of quark–gluon plasma (QGP) have been studied considering it as fractal fluid where configuration space shows a noninteger dimension. The fractal dimension for the QGP droplet is suggested. The thermodynamic properties like radial distribution function, packing fraction, excess entropy and susceptibility are studied. The viscosity of the QGP phase has also been investigated and fluid is found to behave like a superfluid. Some interesting observations are made.
Validity and reliability of positive attitudes toward and perceived importance of wearable display technology as an effective learning tool among nursing students

This study aims to verify the validity and reliability of two separate instruments used for measuring nursing students' positive attitudes toward and perceived importance of wearable display technology as an effective learning tool.Healthcare professionals and undergraduates, including nursing students, use wearable display technologies, such as smart glasses and head-mounted displays, as learning tools. However, the validation of the instruments used to evaluate the perception of such use is insufficient.This methodological study assessed an instrument used to measure nursing students' positive attitudes toward and perceived importance of wearable display technology.In total, 284 students from eight universities in six regions across South Korea participated in this study. The collected data were analyzed using SPSS/WIN 25.0 and Amos 22.0. The construct validity (exploratory factor analysis, confirmatory factor analysis and group comparison) and reliability (internal consistency) were analyzed.Three factors and thirteen items related to positive attitudes toward wearable displays were extracted through exploratory factor analysis, with a cumulative explanatory power of 66.22%. One factor and six items were extracted, with an explanatory power of 74.61% for the perceived importance of the wearable display subscale. In the group comparison, differences in the positive attitudes toward and perceived importance of wearable display technology were observed according to the level of satisfaction with college life, level of satisfaction with the nursing major, whether the participants owned a wearable display and whether they would consider purchasing a wearable display. Each factor loading was adequate, according to the confirmatory factor analysis results. Cronbach's α for positive attitudes toward wearable displays was.79, while that for perceived importance of wearable displays was.93.The developed instrument can be used to evaluate nursing students' positive attitudes toward and perceived importance of wearable display technology as an effective learning tool in the future. This study can serve as a basis for developing and applying wearable display technologies in nursing education.
재직 여성의 프로티언 경력태도와 주관적 경력성공의 관계에서 경력계획의 매개효과

This study was conducted to confirm the mediating effect of career planning in the relationship between protean career attitude and subjective career success of employed women. It was noted that as the number of cases of women stopping economic activities due to working conditions has recently increased, efforts by the state and society to prevent this are being emphasized. To this end, an online survey was conducted on women in area C from March 6 to April 10, 2023, to secure a total of 159 copies of data, and hierarchical regression analysis and bootstrapping were conducted. As a result of the study, it was confirmed that the protean career attitude of employed women had a positive effect on subjective career success, and career planning mediated them. Based on the results of the study, it was suggested that learning and self-management support is needed to improve the protean career attitude of employed women, and that they should be able to design and implement a series of processes to constantly recognize and grow their careers while in office. At the same time, it was emphasized that approval and support for the career development process should be made together to respond to psychological and emotional problems that appear in the career development process.
대학 성인학습자의 기대 학습성과의 차이 분석 : 개인배경을 중심으로

The purpose of this study was to analyze the differences in expected learning outcomes of adult learners in higher education based on their personal backgrounds. To achieve this goal, the study initially explores the expected learning outcomes of adult learners participating in university lifelong learning programs. Subsequently, it analyzes how each outcome differs according to their personal backgrounds and derives meaningful implications. The study subjects comprise adult learners from 30 universities, encompassing both general and vocational institutions. These participants are engaged in the university's lifelong education system support project. The survey was conducted online from March 5th to March 25th, 2021, in collaboration with the National Institute for Lifelong Education and the participating universities. A total of 592 adult learners from 20 universities responded to the survey. Regarding the analysis method, the collected questionnaire data underwent text analysis using Python 3 (ipykernel). Additionally, a chi-square test was performed to identify differences in responses to the expected learning outcomes based on personal backgrounds. The findings revealed that the highest frequency of anticipated learning outcomes for adult learners engaged in university continuing education courses was achieving a degree, followed by obtaining a certificate, gaining professional knowledge, enhancing job skills, pursuing career changes, and seeking personal enjoyment or self-actualization. Furthermore, significant variations in expected learning outcomes were observed in relation to adult learners' personal backgrounds, including institute type, gender, age, final level of education, marital status, employment status, total work experience, and monthly household income. Based on the study's outcomes, policy implications were formulated to facilitate the implementation of personalized education approaches for adults within the university setting.
General Barrier Theorems

Abstract This chapter looks at how to generalise the technique applied in the previous two chapters. It generalises the idea of model-extension and future-switch fragility to a property of R-fragility over a set of models (for arbitrary binary relations R) and proves a General Barrier Theorem that has each of the previous two barriers as instances.
Normativity

Abstract This chapter formulates and proves Hume’s Law. It brings together the work on complex logics from the previous two chapters and lays out a deontic-alethic modal logic, defines descriptive and normative sentences using narrow model-theoretic properties (breakability and unbreakability with respect to the intermodel relation of S-shifting) and uses these to formulate and prove the barrier theorem. The barrier and the definitions are used to diagnose a list of purported counterexamples to the law.
Research on Movie Reviews based on Network Communication

This article analyzes the popularity and audience scope of movie reviews, and reveals the important position of movie reviews in online communication. Conduct an in-depth analysis of the diverse forms of film reviews, including written reviews, film review videos, social media discussions, etc., and explore their impact on review content and audience engagement. Study users' participation levels, interaction forms and opinion exchanges in online movie reviews, and reveal users' roles and behavioral characteristics in reviews. It also explores the impact of fake reviews and trolls on the authenticity and credibility of reviews, as well as the impact of information overload and screening difficulties on audience selection and decision-making. Finally, an information review mechanism and personalized recommendation technology to strengthen online movie reviews are proposed to help users better filter and obtain movie review information that meets their preferences.
Motorcycle Blind Spot Detection Through Computer Vision Techniques

The rise in vehicles on our roads has led to a noticeable increase in traffic accidents, especially those involving motorcycles. Motorcyclists face many problems, one significant issue being unsafe lane changes. In this research, a real-time blind spot detection system is being proposed. The proposed system is aimed at accurately identifying vehicles in the motorcycle's blind spot, thereby enhancing road safety without requiring the rider to divert their attention from the path ahead. This research addresses the limitations of the pre-trained YOLO model, such as vehicle detection during the night, by implementing a custom dataset. This adaptation enhances both precision and recall without compromising real-time inference speeds. The experimental process entailed capturing video from a helmet-mounted camera, segmenting the footage into train, validation, and test splits, annotating the video frames, and finally evaluating both pre-trained and custom-trained models against the test dataset to gather necessary metrics. Despite the custom dataset's relatively smaller size compared to the comprehensive COCO dataset, the custom model demonstrated marked improvement in night-time vehicle detection compared to the pre-trained model, with respective precisions of 82.6% and 22.1%. Additionally, the recall stands out as a significant metric, with the custom model providing a high value of 80.3%, a substantial improvement over 0.4% by the pre-trained model.
Detection of Tea Box Orientations in Retail Shelves Images

This paper presents a new deep learning-based method for the detection of tea box orientations in shelf images. Due to the lack of data, we had to generate synthetic datasets using the Blender rendering framework. This is done by first hand-crafting a 3D model of a retail shelf which is then stocked with a selection of various tea boxes. These 3D models of the tea boxes are obtained by the process of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3D}$</tex> scanning and uploading the scans to the Blender rendering software. Next, the behavior of customers is simulated using Markov chains, and therefore some tea boxes are removed from the shelf. While stocking the tea boxes they are randomly oriented for the sake of dataset variance maximization. Finally, 10000 virtual retail scenes are rendered and annotated in three different annotation sets. We train the YOLOv5 object detection model on the synthetic datasets and achieve an impressive result of up to 84.2% mean average precision. This experimental result shows high promise for future research in this field of object pose estimation in dense scene images.