Reviewerly/exHarmony huggingface dataset:
Accident analysis for Port Terminals using Heterogeneous XAI-informed Machine Learning Approach

Numerous trucks and container cargo are shifting in port areas, and special attention is required for safety management to prevent accidents. Particularly, in ‘port terminal’, the severity of the accident can be high in terms of human damage. Previous studies attempt to predict and detect port accidents, however, research on deriving major factors influencing port-accident severity and developing accident severity prediction models in port areas are rare. Therefore, to prepare safety management strategies, it is necessary to derive risk factors that affect the accident severity in each port area. This study developed an accident severity model using both statistical and machine learning techniques, including the Multilevel Bayesian logit model, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). In data preparation, oversampling and clustering techniques are applied, which can augment data and separate accident risk situations to any characteristics of port accident data. As a consequence, port-accident severity models are developed with heterogeneous accident data. In this process, a severity model suitable for the characteristics of Korean ports was established by considering the heterogeneity of accident data. After developing the severity model, an explainable artificial intelligence (XAI) was used to find key variables affecting port-accident occurrence.
Experiments on the Survivability of Extremophiles

Abstract The ability of terrestrial microorganisms to survive extreme conditions is an important consideration for developing and determining mission criteria, especially for future sample return missions. Of the numerous extreme conditions in space, the effect of impact is an important one for planetary protection. Impacts are complex phenomena with conditions that vary widely but typically involve high stresses sustained for short times. We aim to adapt the pressure-shear plate impact experiment to investigate how impact stresses affect the survival of microorganisms (in particular, extremophiles). Experiments are underway on Deinococcus radiodurans to validate the experimental design and establish testing protocols for such extremophiles.
Research on Automatic Start-up and Debugging Method of Electrical Engineering Equipment Based on Apriori Algorithm

With the improvement of the level of electrical engineering, the requirements for automation are increasing. As the most important equipment, it is necessary to pay attention to the debugging of related equipment to ensure the safety of equipment operation. At the same time, it is necessary to further strengthen the monitoring of equipment operation, realize the automation level of electrical equipment through new technologies, and achieve the development goal of electrical intensification and intelligence, so as to better promote the production quality of electrical equipment. Based on the investigation and analysis of traditional automatic debugging methods of electrical engineering, this paper puts forward an automatic startup debugging method of electrical engineering equipment based on Apriori algorithm, so as to better ensure the safety and stability of equipment operation. Through the research, it is found that the accuracy rate of the automatic start-up debugging method of electrical engineering equipment of this debugging method is higher than that of the traditional debugging method, and the highest accuracy rate of this debugging method can reach 95% in the detection process. While the traditional debugging method is only 63%. By comparing the results of error rate, we can also see that the error rate of this debugging method is obviously lower than that of the traditional debugging method. It can be seen that the debugging method in this paper has higher debugging effect than the traditional method.
Topic Search Algorithm of Network Multimedia Educational Resources Based on Artificial Intelligence

With the increasing scale of multimedia educational resources on the Internet, it is difficult to quickly retrieve resources that meet your needs. In order to accurately search multimedia educational resources from web pages, this paper focuses on the topic search algorithm, and introduces artificial intelligence technology to design a topic search system for network multimedia educational resources to realize intelligent retrieval of resources. For the three proposed topic search algorithms, this paper evaluates the algorithm performance. The evaluation experiment results show that the HITS search algorithm has the highest precision rate and recall rate, and the Topic-PageRank algorithm is the lowest. For different types of educational resource topics, the HITS search algorithm still has the highest search accuracy.
Efficient $(3,3)$-isogenies on fast Kummer surfaces

We give an alternative derivation of $(N,N)$-isogenies between fastKummer surfaces which complements existing works based on the theory oftheta functions. We use this framework to produce explicit formulae for thecase of $N = 3$, and show that the resulting algorithms are more efficient thanall prior $(3, 3)$-isogeny algorithms.
Programmed differently? Testing for gender differences in Python programming style and quality on GitHub

Abstract The underrepresentation of women in open-source software is frequently attributed to women’s lack of innate aptitude compared to men: natural gender differences in technical ability (Trinkenreich et al., 2021). Approaching code as a form of communication, I conduct a novel empirical study of gender differences in Python programming on GitHub. Based on 1,728 open-source projects, I ask if there is a gender difference in the quality and style of Python code measured in adherence to PEP-8 guidelines. I found significant gender differences in structure and how Python files are organized. While there is gendered variation in programming style, there is no evidence of gender difference in code quality. Using a Random Forest model, I show that the gender of a programmer can be predicted from the style of their Python code. The study concludes that gender differences in Python code are a matter of style, not quality.
Geometric versus Entropic Gaussian Correlations in an Open Quantum System of Two Bosonic Modes

Different types of geometric and entropic quantum correlation quantifiers are studied for a system composed of two resonant bosonic modes embedded in a thermal bath. The description of the evolution of the correlation measures is formulated in the framework of the theory of open systems, based on completely positive quantum dynamical semigroups, by using both a geometric and entropic quantification of total nonclassical correlations of Gaussian states. We consider the special case when the initial squeezed thermal state of the system preserves its form in time. We show that time evolution of the measures strongly depends on the parameters characterising the initial state of the system (squeezing parameter and average thermal photon numbers of the two modes) and of the thermal environment (temperature of the thermal bath and dissipation rate). In the limit of large times all the considered measures asymptotically tend to zero value, corresponding to an asymptotic bimodal uncorrelated product state. We make a comparison between the behaviour of the evolution in time of the Gaussian geometric quantum correlations and Gaussian entropic quantum correlations.
Cloud-Edge Collaborative Human Activity Recognition System Design and Implementation

This paper proposes an edge computing architecture for Human Activity Recognition (HAR) based on Raspberry Pi and HaaS100, aimed at enhancing real-time performance and system robustness. The proposed approach employs Edge-End inference within the communication range of edge nodes, while relying on Cloud Edge inference otherwise, thereby establishing a comprehensive Cloud Edge End system that leverages the capabilities of a mobile communication 5G private network Wi-Fi. To achieve accurate activity recognition, three algorithms, namely Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), and Residual Network-based CNN (ResNet-based CNN), are implemented and their performances are rigorously compared using Actitracker datasets. The experimental results demonstrate that the ResNet-based CNN exhibits superior performance, achieving an impressive F1-score of 0.977. Additionally, to address the challenge of model size and resource-constrained edge devices, model compression is employed through channel-wise data collection, effectively reducing the parameter size from 220,000 to 160,000, with a negligible 0.5% decrease in accuracy. Finally, the compressed model is deployed on edge devices, and retraining is conducted using self-collected datasets to ensure practicality and adaptability in real-world scenarios. The proposed HAR edge computing architecture showcases significant advancements in real-time performance, system robustness, and resource optimization, making it a promising solution for activity recognition in edge computing environments.
Investigating a cinematic virtual reality narrative framework for screenwriting

Over the last decade cinematic virtual reality (CVR) has been progressively developing as a meaningful vehicle for impactful and immersive narratives. Recent studies of CVR concepts and components have laid the ground for a CVR narrative theoretical framework that might assist researchers and practitioners to understand this type of virtual reality (VR) experience. While existing studies have isolated key features of CVR, a range of projects from different fields of work or in different stages of production have utilized a range of different screenwriting processes and strategies to address the affordances of this medium. In this article we seek to systematize the key findings of earlier studies into a narrative framework for CVR and to analyse how this framework is reflected in existent models and templates for writing a screenplay for CVR experiences. Furthermore, and based on this narrative framework, we also aim to contribute an exploratory approach to CVR screenwriting by proposing a variative and original screenwriting template. This template addresses the main limitations of the existent screenwriting templates and formats that we analyse in this study while also summoning the main advantages.
Addressing the Digital Divide in Indonesian Higher Education: Insights, Implications, and Potential Solutions

The digital divide in Indonesian higher education has become a pressing issue, particularly during the COVID-19 pandemic, where access to technology and online resources is crucial for educational continuity. This systematic literature review (SLR) aims to provide comprehensive insights into the factors contributing to the digital divide, its implications for educational outcomes, and potential solutions to overcome this divide. The findings of the SLR strongly support the significance of addressing the digital divide in Indonesian higher education. Key strategies to bridge this divide include investing in improved internet infrastructure, equalizing technological resources, and providing access to digital devices and facilities. The implications of this study are of utmost importance for policymakers, educational institutions, and practitioners. Prioritizing infrastructure development and reliable internet connectivity is essential, along with efforts to narrow the technological gap between regions in Indonesia, ensuring equal access to digital resources and tools. Comprehensive technical training and support programs for educators are crucial to enhance their skills in managing online-based learning, enabling them to effectively utilize digital resources, facilitate online discussions, and engage students in collaborative learning activities. Equally important is providing training and support for students to develop digital skills, promoting digital literacy and equal access to educational opportunities. Future research should prioritize identifying effective strategies and best practices for implementing technology-enabled distance learning.
CHIM—Chatbot in the Museum

Oliver Gustke, Stefan Schaffer, Aaron Ruß, CHIM-Chatbot in the Museum: Exploring and Explaining Museum Objects with Speech- Based AI CHIM-Chatbot in the Museum was a research project during which we developed a chatbot prototype that is able to provide answers to users' questions about museum objects. CHIM was developed by Linon Medien KG and the Deutsches Forschungs- zentrum für Künstliche Intelligenz (DFKI). The interactive conversation system was implemented as an Android- based demonstration app and tested at the Städel Mu- seum, Frankfurt am Main, in the spring of 2022. The results showed that upcoming chatbot- based systems might motivate and encourage museum visitors and contri- bute to participation.
HAlf-MAsked Model for Named Entity Sentiment analysis

Named Entity Sentiment analysis (NESA) is one of the most actively developing application domains in Natural Language Processing (NLP). Social media NESA is a significant field of opinion analysis since detecting and tracking sentiment trends in the news flow is crucial for building various analytical systems and monitoring the media image of specific people or companies. In this paper, we study different transformers-based solutions NESA in RuSentNE-23 evaluation. Despite the effectiveness of the BERT-like models, they can still struggle with certain challenges, such as overfitting, which appeared to be the main obstacle in achieving high accuracy on the RuSentNE-23 data. We present several approaches to overcome this problem, among which there is a novel technique of additional pass over given data with masked entity before making the final prediction so that we can combine logits from the model when it knows the exact entity it predicts sentiment for and when it does not. Utilizing this technique, we ensemble multiple BERTlike models trained on different subsets of data to improve overall performance. Our proposed model achieves the best result on RuSentNE-23 evaluation data and demonstrates improved consistency in entity-level sentiment analysis.
The Consequence of Proficient Mind-set of Public Secondary School Teachers on Their Teaching Venues

The worth mentioning objectives of the study were; to determine the proficient mind-set-of teachers towards their profession, and to find out the difference in proficient mind-set-of secondary school teachers based on gender, The Positivist paradigm was followed to design a quantitative research method for collecting evidence to support the study. A sample of 720 teachers from three divisions of Punjab i.e. Lahore, Multan, and Rawalpindi was selected through random sampling. To collect information from teachers’ two instruments i.e. Proficient mindset scale and the Teaching venues scale were developed. The study was descriptive and a survey was used to collect data. The data were analyzed through descriptive and inferential statistics. Frequency and percentage of demographic information were computed to explore the distribution of gender, and over locale. Multiple regressions were applied followed by the administration of Pearson product-moment correlation to find and predict the effects of proficient mind-set-of public secondary school teachers on their teaching venues. Proficient mind-set-of teachers explained 42% variance in teacher venues.
Energy-Efficient Cloud Computing

The summary on Energy-Efficient Cloud Computing delves into the vital exam of the environmental implications associated with cloud computing and proposes techniques to enhance the power efficiency of cloud records facilities. In this complete exploration, the focal point is on mitigating the carbon footprint of cloud technology, emphasizing the utilization of environmentally sustainable practices and revolutionary technology. The environmental effect of cloud computing has turn out to be a urgent subject as facts facilities contribute considerably to power intake and greenhouse gasoline emissions. This research scrutinizes the present day state of energy consumption in cloud information facilities and evaluates its environmental outcomes. The goal is to identify possible answers that not most effective mitigate the ecological footprint however also align with the developing call for for scalable and green cloud offerings. A pivotal issue of the proposed techniques includes the implementation of green records centers. These records facilities integrate eco-friendly technology, which include renewable strength sources and superior cooling structures, to decrease energy intake and decrease environmental effect. By exploring the feasibility and scalability of inexperienced records centers, the research ambitions to provide a sustainable version for future cloud infrastructure improvement. Optimization algorithms become a key era to enhance electricity performance in cloud computing. The look at investigates the application of algorithms designed to optimize resource usage, workload distribution, and normal device overall performance. Through the implementation of these algorithms, the research seeks to enhance the operational performance of cloud statistics centers, lowering power waste and promoting a extra sustainable computing infrastructure. Energy-conscious resource allocation is another essential detail addressed inside the abstract. The studies explores mechanisms to dynamically allocate assets primarily based on real-time power consumption information. By incorporating strength-cognizance into resource management, the goal is to strike a stability among overall performance requirements and electricity performance, ensuring most effective usage of assets whilst minimizing the environmental effect In conclusion, this abstract units the degree for a comprehensive exploration of Energy-Efficient Cloud Computing, losing mild at the environmental challenges posed with the aid of cloud statistics facilities. Through the investigation of green records centers, optimization algorithms, and power-aware resource allocation, the studies aspires to provide practical solutions that harmonize cloud computing with ecological sustainability, fostering a greater electricity-green and environmentally responsible cloud infrastructure.
Explainable AI(XAI): A Review

Explainable AI (XAI) has emerged as a essential region of research within the realm of device getting to know, that specialize in improving the interpretability and comprehensibility of complex fashions. The opacity of many device getting to know algorithms, specially deep neural networks, has raised worries regarding the transparency and responsibility of computerized choice-making systems. This research targets to delve into various methodologies geared toward making those fashions greater interpretable, understandable, and in the long run extra straightforward. The primary motivation in the back of this research lies inside the imperative to bridge the space among the inherent complexity of superior device studying models and the need for obvious decision-making methods. Achieving explainability is vital for gaining user agree with, ensuring regulatory compliance, and facilitating the adoption of AI systems in touchy domains which include healthcare, finance, and crook justice. One road of exploration entails growing novel techniques for model interpretation, permitting stakeholders to understand the cause behind a model's predictions. This could consist of growing visualization gear that offer insights into characteristic significance, decision obstacles, and universal model conduct. Another component below scrutiny is the incorporation of inherently interpretable models or the amendment of present complex fashions to render them extra obvious with out compromising performance. Furthermore, the research scrutinizes the alternate-off among version complexity and interpretability, aiming to strike a balance that ensures both accuracy and explainability. Ethical concerns related to bias and fairness in interpretable AI models also are examined, acknowledging the significance of keeping off unintentional results in choice-making processes. As the deployment of AI structures turns into more and more pervasive, the effects of this studies are poised to have a huge impact at the responsible and ethical use of synthetic intelligence. By dropping mild on the inner workings of these structures, explainable AI contributes to constructing a basis of trust among users, builders, and society at big, fostering a greater obvious and accountable era within the application of system studying technology.
Autocorrelations Decay in Texts and Applicability Limits of Language Models

We show that the laws of autocorrelations decay in texts are closely related to applicability limits of language models. Using distributional semantics we empirically demonstrate that autocorrelations of words in texts decay according to a power law. We show that distributional semantics provides coherent autocorrelations decay exponents for texts translated to multiple languages. The autocorrelations decay in generated texts is quantitatively and often qualitatively different from the literary texts. We conclude that language models exhibiting Markovian behavior, including large autoregressive language models, may have limitations when applied to long texts, whether analysis or generation.
Augmentation methods for spelling corruptions

The problem of automatic spelling correction is vital to applications such as search engines, chatbots, spellchecking in browsers and text editors. The investigation of spell-checking problems can be divided into several parts: error detection, emulation of the error distribution on the new data for model training, and automatic spelling correction. As the data augmentation technique, the adversarial training via error distribution emulation increases a model’s generalization capabilities; it can address many other challenges: from overcoming a limited amount of training data to regularizing the training objectives of the models. In this work, we propose a novel multi-domain dataset for spelling correction. On this basis, we provide a comparative study of augmentation methods that can be used to emulate the automatic error distribution. We also compare the distribution of the single-domain dataset with the errors from the multi-domain and present a tool that can emulate human misspellings.