Reviewerly/exHarmony huggingface dataset:
Digitally Supported Learning Processes in Schools: Teachers’ Implicit Theories on Learning Activities with Technology

Technology is often not used profitably to promote teaching and learning processes. Implicit theories influence the use of technology in teaching. So far, these have been insufficiently explored against the background of the ICAP framework. This study addressed this research gap and investigated teachers’ implicit theories of quality use of technology. Semi-structured expert interviews were conducted with N = 12 teachers from different types of secondary schools in Germany. The results show that the use of technology for passive engagement is justified by the format of the technology, the active engagement by the learning outcome, the constructive engagement by information and communication technology (ICT) literacy promotion and the interactive engagement by students’ cooperation. Further research should describe implicit theories by the way learner-centered teaching is implemented with technology.
Navigating the New Frontier of Finance, Art, and Marketing

This chapter explores the intersection of finance, art, and marketing in the new frontier of digital assets, specifically focusing on cryptocurrencies, NFTs, and the metaverse. The authors analyze the potential impact of these innovative technologies on traditional financial systems, art markets, and advertising strategies. Through a comprehensive examination of the concepts of blockchain, virtual reality, and decentralization, this chapter aims to provide insight into how these emerging digital assets can reshape the way people perceive and interact with financial, artistic, and marketing landscapes.
UWYOLOX: An Underwater Object Detection Framework Based on Image Enhancement and Semi-supervised Learning

Deep learning-based underwater optical object detection is of great significance to underwater environment exploration and underwater scientific research. However, the quality of underwater images obtained by optical imaging devices is poor and the available labeled data is lacking lead to lower mean Average Precision of underwater object detection. Therefore, in this paper, an novel underwater object detection framework, UWYOLOX, is proposed to solve the problems mentioned above. The key is to present a joint learning-based underwater image enhancement module (JLUIE) and an improved semi-supervised learning method USTAC (Underwater STAC) for underwater object detection. Firstly, JLUIE and YOLOX-Nano share the detection loss for training, so that JLUIE can adaptively enhance each image for better detection performance. Then, USTAC, which can make full use of unlabeled data for training, is introduced to further improve the mean Average Precision of object detection. Experimental results show that the mAP of UWYOLOX is 1.77% higher than YOLOX-nano, which demonstrates the effectiveness of UWYOLOX. Moreover, USTAC can improve the mAP of the model obtained by supervised learning using 800 labeled data by 7.46%. Thus the effectiveness of USTAC is further demonstrated.
Additional Theorems

Kirchhoff’s laws, parallel and series resistor equivalents, along with the superposition principle, will go a long way toward determining the voltage-current relationships in many simple circuits involving resistors and ideal sources.
Business Models of an AI Marketplace for Energy Systems with Focus on Demand Response

Three marketplace archetypes of increasing complexity are conceptualised with business models for services that support the implementation of demand response programs.
Blockchain Technologies and Building/City Information Modelling

The advent of smart technologies and their application in the Architectural, Engineering, and Construction (AEC) domain has been extensively documented; however, the development of Blockchain technologies (BCT) is a concept that is relatively new to many fields. In design and construction, there is a plethora of literature but very few practical use cases. Following an exploration of substantive scholarly contributions on relationships between Building Information Modelling (BIM) and blockchain technologies and between Building Information Modelling and City Information Modelling (CIM), this research investigates the potential of a tripartite interface between BCT, BIM, and CIM using Distributed Ledger Technology (DLT). The study proposes a conceptual framework to facilitate geo-specific value exchanges and asset management within the urban space akin to the exchange of cryptocurrency within the financial space. This framework is validated with simulated data. With the use of a permissioned blockchain, the suggested framework offers streamlined management of physical assets by leveraging the existence of digital models and representations of these assets. Moreover, the paper concludes with a discussion of the benefits and limitations of the research.
MEANS OF MASS COMMUNICATION AND THE HIERARCHY OF SOCIETY: INTERACTION FACTORS

Mechanisms of interaction between mass communication and the hierarchy of society are considered. It was found that as a result of the action of the mechanisms of mass communication effects and counter-effects, society is faced with a double process. The possibility of personalized information flow offered by digital media provides ample space for underprivileged social groups to choose media content that matches their own construction of reality. Environments in this context significantly contribute to the strengthening of the heterarchical features of society and the semiotic power of each representative of the audience against the meanings permitted from above. However, the circumstances under which this clearly democratic characteristic of mass communication practice leads to the fact that democratized social groups become increasingly large and face only such communication content that corresponds to the philosophy and expectations of their group or their environment are also investigated. This worldview, mediated by means of mass communication, transformed into a real construction of its environment, contains a danger, because it does not condition the skills with the help of which other clusters of society, which are differently positioned in the social hierarchy, can gain the worldview experience of this social group and vice versa. It is argued that, in this sense, the means of mass communication can play a significant role in creating social inequality in the hierarchical structure of society. Keywords: cluster, communication, content, hierarchy, media, society.
An Intelligent Learning System Based on Robotino Mobile Robot Platform

The fourth industrial revolution (Industry 4.0) has attracted more and more attention in education and research activities toward the fields of mechatronics and information technology. In this context, developing an interactive learning system for achieving the effectiveness of education and research activities is increasingly focused by lecturers, scientists, and educational company. Therefore, this paper presents an intelligent learning system that is designed based on Robotino robot, a mobile robot developed by Festo Didactic, for education, training, and research activities. Taking the great advantages of new learning system, users are able to develop their knowledge and practical skills when working with the mobile robot and its applications.
Improving Usability Services and User Experience of Online Museum Virtual Exhibition with TRIZ Theory

This study explores users' perceptions of a digital museum's online virtual exhibition. A survey and interviews have been conducted to collect data on user opinion and the ultimate objective was to determine what users think of the museum's online virtual exhibition services and what suggestions they provide for improvement and personalization. TRIZ theory contradiction matrix method was used to modify and improve the usability services and user experience of the Shaanxi History Museum's "World Heritage Photography Art Exhibition" virtual exhibition by analyzing data and identifying problems from a user-centered perspective. The results of this study recommend five TRIZ principles for the development of conceptual solutions for increased user engagement, improved usability services, and better user experience for the museum's online virtual exhibitions. These findings suggest that the successful application of TRIZ theory can enhance user experience as it helps to solve complex problems in an organized and systematic way.
Accessibility of covariance information creates vulnerability in Federated Learning frameworks

Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side.We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks.The code examples are provided at GitHub (https://github.com/manuhuth/Data-Leakage-From-Covariances.git). The CNSIM1 dataset, which we used in the manuscript, is available within the DSData R package (https://github.com/datashield/DSData/tree/main/data).
Why Pandemics and Climate Change Are Hard to Understand and Make Decision-Making Difficult

Abstract This paper draws on diverse psychological, behavioural and numerical literature to understand some of the challenges we all face in making sense of large-scale phenomena and use this to create a road map for HCI responses. This body of knowledge offers tools and principles that can help HCI researchers deliver value now, but also highlights challenges for future HCI research. The paper is framed by looking at patterns and information that highlight some of the common misunderstandings that arise—not just for politicians and the general public but also for many in the academic community. This paper does not have all the answers to this, but we hope it provides some and, perhaps more importantly, raises questions that we need to address as scientific and technical communities.
Three roads back: how Emerson, Thoreau, and William James responded to the greatest losses of their lives

"Three roads back: how Emerson, Thoreau, and William James responded to the greatest losses of their lives." Mortality, ahead-of-print(ahead-of-print), pp. 1–2 Notes1. ‘Resilience’ was the original title of the book (Richardson, Citation2023, p. ix).
Auto encoder with mode‐based learning for keyframe extraction in video summarization

Abstract The exponential increase in video consumption has created new difficulties for browsing and navigating through video more effectively and efficiently. Researchers are interested in video summarization because it offers a brief but instructive video version that helps users and systems save time and effort when looking for and comprehending relevant content. Key frame extraction is a method of video summarization that only chooses the most important frames from a given video. In this article, a novel supervised learning method ‘TC‐CLSTM Auto Encoder with Mode‐based Learning’ using temporal and spatial features is proposed for automatically choosing keyframes or important sub‐shots from videos. The method was able to achieve an average F‐score of 84.35 on TVSum dataset. Extensive tests on benchmark data sets show that the suggested methodology outperforms state‐of‐the‐art methods.
Webly-supervised semantic segmentation via curriculum learning

In this paper, we propose a weakly supervised semantic segmentation method by directly learning from web images, which are crawled from the Internet by using text queries, without any explicit user annotation or even data filtering. With the goal of handling the massive amount of noisy labels in web images, we design a three-stage approach for weakly-supervised semantic segmentation based on curriculum learning. We first generate pixel-level masks for the training images via a popular weakly-supervised semantic segmentation framework. Then, we consider the noise of the web data in two ways. At the image-level, the complexity of data is measured using its distribution density in a classification feature space. At the pixel-level, the complexity of the mask is evaluated by exploiting the relationship between the saliency map and those segmented images in an unsupervised manner. The key insight to this design is that, common and simple object patterns in images should be salient with both the saliency detector and weakly supervised DCNNs, where they should be sparse with high regional consistency between them. This allows for an efficient implementation of curriculum learning from noisy web images. Experiments on the popular PASCAL VOC 2012 benchmark show that we achieve very competitive performance with scores of 64.0% mIoU using our pure web dataset, which contains noisy, single-label images. We further improve the performance to 69.7% mIoU by using the CurriculumWebSegNet fine-tuned on the PASCAL VOC dataset, which has more precise multi-label supervision.
The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition

The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. However, although many established infrastructures provide comprehensive and long-term stable services and platforms, a large quantity of research data is still hidden. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, for example, time series of images or high-resolution hyperspectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institutional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements an on-premise approach, which allows research data to be kept in place and wrapped in FAIR-aware software infrastructure. In this chapter, the e!DAL infrastructure software and the PGP repository are presented as best practice on how to easily setup FAIR-compliant and intuitive research data services.
Less is more: information needs, information wants, and what makes causal models useful

Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions. We test the boundaries of this effect (Experiment 4), finding that including a small amount of information beyond that related to the target answer has a detrimental effect. Finally, we examine whether people know what information they need (Experiment 5). We find that simple, targeted, information still leads to the best decisions, while participants who believe they do not need information or seek out the most complex information performed worse.
The validation of online webcam-based eye-tracking: The replication of the cascade effect, the novelty preference, and the visual world paradigm

The many benefits of online research and the recent emergence of open-source eye-tracking libraries have sparked an interest in transferring time-consuming and expensive eye-tracking studies from the lab to the web. In the current study, we validate online webcam-based eye-tracking by conceptually replicating three robust eye-tracking studies (the cascade effect, n = 134, the novelty preference, n = 45, and the visual world paradigm, n = 32) online using the participant's webcam as eye-tracker with the WebGazer.js library. We successfully replicated all three effects, although the effect sizes of all three studies shrank by 20-27%. The visual world paradigm was conducted both online and in the lab, using the same participants and a standard laboratory eye-tracker. The results showed that replication per se could not fully account for the effect size shrinkage, but that the shrinkage was also due to the use of online webcam-based eye-tracking, which is noisier. In conclusion, we argue that eye-tracking studies with relatively large effects that do not require extremely high precision (e.g., studies with four or fewer large regions of interest) can be done online using the participant's webcam. We also make recommendations for how the quality of online webcam-based eye-tracking could be improved.
Variability and Trend Analysis of a Grid-Scale Solar Photovoltaic Array above the Arctic Circle

As solar photovoltaic (PV) power generation continues to grow in popularity, the variability in solar irradiance caused by weather effects such as clouds poses an increasing challenge to maintaining grid stability. Characterizing the variability and trends present in historical PV production data is vital to the development of effective models for predicting rapid changes. This is particularly important at higher latitudes where seasonal changes in PV generation are more extreme. In this paper, we analyse data from a small, grid-scale PV array in Kotzebue, Alaska (66.8969° N, 162.5931° W), located above Arctic Circle. We also successfully validate the variability index (VI), a previously proposed metric which quantifies the volatility of solar PV data over a given time span using a synthetic cloudless (clear-sky) dataset as a reference. We also include an examination of the stationarity of the PV production data at various timescales as well as the efficacy of using clear-sky models as a reference for de-trending solar irradiance data via, showing that better results can be obtained from data closer to solar noon. To the best of our knowledge, this is the first use of VI to assess PV production data from above the Arctic Circle.
On the Way to a Framework for Evaluating Creativity in Requirements Engineering

Creativity in requirements engineering (RE) has recently emerged to help innovate “novel and useful” requirements and improve a software system’s sustainability. Existing research has mostly focused on workshops, techniques, and tools to aid creative requirements elicitation. Limited attention, however, has been dedicated to creativity evaluation, despite the current mechanisms being largely restricted to rating requirements for a broad notion of “novelty and appropriateness”. In addition, such mechanisms focus on evaluating creativity from an elicitation perspective, leaving other RE activities widely disregarded. To further advance the literature, we present a preliminary study on developing a framework that aims to evaluate creativity in a precise manner and accounts for the full spectrum of RE activities. In particular, we propose a “creative requirement diagnosis scale (CRDS)” that includes 27 indicators to assess creativity, present a novel framework to evaluate the creative merits of requirements in terms of the complete RE process, and further evaluate requirements using our framework in a study with 53 participants. The results suggest our framework’s potential to capture creativity aspects that would otherwise be undetected by traditional techniques. Our study also indicates the need for further refinement of the framework, thereby opening new avenues for creativity in RE.
Comparing Open Arabic Named Entity Recognition Tools

The main objective of this paper is to compare and evaluate the performances of three open Arabic Named Entity Recognition (NER) tools: CAMeL, Hatmi, and Stanza. We collected a corpus consisting of 30 articles written in Modern Standard Arabic (MSA) and manually annotated all the entities of the person, organization, and location types at the article (document) level. Our results suggest a similarity between Stanza and Hatmi with the latter receiving the highest F1 score for the three entity types. However, CAMeL achieved the highest precision values for names of people and organizations. Following this, we implemented a “merge” method that combined the results from the three tools and a “vote” method that tagged named entities only when two of the three identified them as entities. Our results showed that merging achieved the highest overall F1 scores. Moreover, merging had the highest recall values while voting had the highest precision values for the three entity types. This indicates that merging is more suitable when recall is desired, while voting is optimal when precision is required. Finally, we collected a corpus of 21,635 articles related to COVID-19 and applied the merge and vote methods. Our analysis demonstrates the tradeoff between precision and recall for the two methods.
Hybrid Convolutional Autoencoder-Hierarchical Clustering Algorithm To Reveal Image Spam Sources

We propose a novel hybrid algorithm framework to address the problem of clustering images received in spam emails based on authorship. The multimodal nature of these images, containing foreground objects, text, or a combination of both, poses a significant challenge for grouping them effectively. To address this challenge, we train convolutional autoencoders (CAE) to extract visual features from the images, which are produced by the encoder of the trained CAEs. Furthermore, we utilize an optical character recognition (OCR) algorithm to extract text information from the images. The extracted text and visual features, in conjunction with layout features, are employed to construct matrices that measure the similarities between each pair of images in our experiment dataset. We subsequently apply a two-stage hierarchical clustering algorithm to cluster the images into groups. We compare the results produced by our proposed algorithm with the ground truth collected by a domain expert. Our experimental findings reveal that our relatively simple CAEs, with as few as thirty-seven visual features, can achieve homogeneity, completeness, and V-measures that are as high as those obtained from more complex convolutional neural networks (CNNs).
Cost Efficient Mammogram Segmentation and Classification with NeuroMem® Chip for Breast Cancer Detection

In this paper, a Computer Aided Diagnosis system to detect and classify anomalies on mammograms is proposed. A segmentation method for anomaly extraction has been proposed using the NeuroMem® Chip NM500 which integrates physical neural networks, up to 83% of the anomalies were detected. We configured two subnetworks for the mammogram classification step the accuracy reached 87%.
A New Similarity Measure and Hierarchical Clustering Approach to Color Image Segmentation

Cluster analysis is an important task in data analysis and machine learning. Traditional clustering methods, such as partitioning and density-based approaches, have limitations in identifying natural clusters in datasets with elliptical and chained shapes. In this paper, we propose a novel hierarchical clustering algorithm for color image segmentation that addresses these limitations by quantifying the degree of overlap between clusters as a similarity measure for the merging process.