Reviewerly/exHarmony huggingface dataset:
The Construction and Application of a Cloud Editing Digital Museum Oriented to Virtual Tour

The COVID-19 pandemic has accelerated the digital transformation of museums, and online virtual tours have become an important development direction for the cultural inheritance of museums in the present and future. Therefore, there is an urgent need for appropriate digital museum spaces to attract virtual tours of visitors. Firstly, the paper introduces the current situation of the digital transformation of museums around the world under the background of the COVID-19 pandemic and analyzes the challenges of digital museums in supporting virtual tours. Secondly, aiming at these challenges, we design a cloud editing digital museum architecture and develop a cloud editing digital museum system based on the requirement survey. Finally, the paper introduces the application cases and effects of the cloud editing digital museum system, such as the Zhejiang University History Museum and the China National Silk Museum, and analyzes them with the SWOT method. The SWOT analysis shows that the cloud editing digital museum has advantages in the virtual tour, such as the rapid construction of virtual museums, cost savings, update and maintenance, gamification experience, multiterminal support, immersion and fun, and maintaining visitors’ loyalty. It can provide a reference for museums’ digital transformation research and cultural inheritance applications.
Taming Diffusion Models for Music-Driven Conducting Motion Generation

Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which integrates the diffusion model to a two-stage learning framework. We further propose a random masking strategy to improve the feature robustness, and use a pair of geometric loss functions to impose additional regularizations and increase motion diversity. We also design several novel metrics, including Frechet Gesture Distance (FGD) and Beat Consistency Score (BC) for a more comprehensive evaluation of the generated motion. Experimental results demonstrate the advantages of our model. The code is released at https://github.com/viiika/Diffusion-Conductor.
Quantifying Deep Learning Model Uncertainty in Conformal Prediction

Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations. We propose a probabilistic approach in quantifying the model uncertainty derived from the produced prediction sets in conformal prediction and provide certified boundaries for the computed uncertainty. By doing so, we allow model uncertainty measured by CP to be compared by other uncertainty quantification methods such as Bayesian (e.g., MC-Dropout and DeepEnsemble) and Evidential approaches.
Digits Recognition for Sign Language Using Transfer Learning Models

Sign language is a universally language for the deaf people. Various deep learning models have been used to classify the dataset images for recognizing the digits. To improve the classifiers `accuracy, this study uses two different transfer learning algorithms, specifically VGG-16 and Resnet for recognizing the digits of sign language. Then in the same dataset, we apply many augmentation techniques to measure the accuracy of the classifiers. The study results have shown that VGG16 achived 95% accuracy and ResNet 93% accuracy.
Human-AI Collaborative Sub-Goal Optimization in Hierarchical Reinforcement Learning

Hierarchical reinforcement learning often involves human expertise in defining multiple sub-goals to decompose complex objectives into relevant sub-tasks. However, manually specifying these sub-goals is labor-intensive, costly, and prone to introducing biases or misleading the agent. To overcome these challenges, we propose a collaborative human-AI algorithm that seamlessly integrates with hierarchical models to automatically update prior knowledge and optimize candidate sub-goals. Our algorithm can be easily incorporated into a wide range of goal-conditioned frameworks. We evaluate our approach in comparison with relevant baselines, we demonstrate the effectiveness of our algorithm in addressing and preventing negative inferences arising from confusing or conflicting sub-goals. Additionally, our algorithm shows robustness across different levels of human knowledge, accelerating convergence towards optimal sub-goal spaces and hierarchical policies.
XGBoost for Interpretable Alzheimer’s Decision Support

Despite their necessity in directing patient care worldwide, simple and accurate diagnostic tools for early Alzheimer’s disease (AD) do not exist. To support healthcare decision-making and planning, this research leverages large, multi-site accessible data and state-of-the-art supervised machine learning (XGBoost) to enable rapid, accurate, low-cost, accessible, non-invasive, interpretable, and early clinical evaluation of AD. Machine learning was employed to combine three key features: Everyday Cognition Questionnaire, Alzheimers Disease Assessment Scale, and Delayed Total Recall, achieving area under the receiver operating characteristic curves scores consistently above 97%. The selected features are important because they are non-invasive and easily collected. Low performance on delayed recall alone appears to distinguish most AD patients, consistent with the pathophysiology of AD where individuals having problems storing new information into long-term memory. Distinguishing this research from existing literature was the focus of enhancing the model's interpretability while maintaining performance of more complex and opaque models. The interpretable model enables understanding of the decision process, vital for clinical adoption of machine learning tools in AD evaluation. In summary, we present a methodology which identified accessible and noninvasive features, each with their absolute thresholds, together with a clinically operable decision route, to accurately and rapidly detect, differentiate, and diagnose Alzheimer's disease patients.
Research on Information Identification of Chaotic Map with Multi-Stability

Influenced by the rapid development of artificial intelligence, the identification of chaotic systems with intelligent optimization algorithms has received widespread attention in recent years. This paper focuses on the intelligent information identification of chaotic maps with multi-stability properties, and an improved sparrow search algorithm is proposed as the identification algorithm. Numerical simulations show that different initial values can lead to the same dynamic behavior, making it impossible to stably and accurately identify the initial values of multi-stability chaotic maps. An identification scheme without considering the initial values is proposed for solving this problem, and simulations demonstrate that the proposed method has the highest identification precision among seven existing intelligent algorithms and a certain degree of noise resistance. In addition, the above research reveals that chaotic systems with multi-stability may have more potential applications in fields such as secure communication.
Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem Under “Overparametrization” Condition

This paper is devoted to solving a convex stochastic optimization problem in a overparameterization setup for the case where the original gradient computation is not available, but an objective function value can be computed. For this class of problems we provide a novel gradient-free algorithm, whose creation approach is based on applying a gradient approximation with $$l_2$$ randomization instead of a gradient oracle in the biased Accelerated SGD algorithm, which generalizes the convergence results of the AC-SA algorithm to the case where the gradient oracle returns a noisy (inexact) objective function value. We also perform a detailed analysis to find the maximum admissible level of adversarial noise at which we can guarantee to achieve the desired accuracy. We verify the theoretical results of convergence using a model example.
Secure Groups for Threshold Cryptography and Number-Theoretic Multiparty Computation

In this paper, we introduce secure groups as a cryptographic scheme representing finite groups together with a range of operations, including the group operation, inversion, random sampling, and encoding/decoding maps. We construct secure groups from oblivious group representations combined with cryptographic protocols, implementing the operations securely. We present both generic and specific constructions, in the latter case specifically for number-theoretic groups commonly used in cryptography. These include Schnorr groups (with quadratic residues as a special case), Weierstrass and Edwards elliptic curve groups, and class groups of imaginary quadratic number fields. For concreteness, we develop our protocols in the setting of secure multiparty computation based on Shamir secret sharing over a finite field, abstracted away by formulating our solutions in terms of an arithmetic black box for secure finite field arithmetic or for secure integer arithmetic. Secure finite field arithmetic suffices for many groups, including Schnorr groups and elliptic curve groups. For class groups, we need secure integer arithmetic to implement Shanks’ classical algorithms for the composition of binary quadratic forms, which we will combine with our adaptation of a particular form reduction algorithm due to Agarwal and Frandsen. As a main result of independent interest, we also present an efficient protocol for the secure computation of the extended greatest common divisor. The protocol is based on Bernstein and Yang’s constant-time 2-adic algorithm, which we adapt to work purely over the integers. This yields a much better approach for multiparty computation but raises a new concern about the growth of the Bézout coefficients. By a careful analysis, we are able to prove that the Bézout coefficients in our protocol will never exceed 3max(a,b) in absolute value for inputs a and b. We have integrated secure groups in the Python package MPyC and have implemented threshold ElGamal and threshold DSA in terms of secure groups. We also mention how our results support verifiable multiparty computation, allowing parties to jointly create a publicly verifiable proof of correctness for the results accompanying the results of a secure computation.
Development of post-editing skills in machine translation

As a necessary complement to machine translation, post-editing plays an increasingly vital role in the language service industry. It represents a crucial pathway for enhancing machine translation quality and serves as a significant hallmark of human-machine interactive translation. With the advancement of machine translation technology and its expanding application scope, the cultivation of post-editing skills has become a crucial component of translation education. Through well-designed training strategies, learners can enhance their post-editing abilities to meet the translation market demands of the machine translation era.
The application of the backwash effect of language testing in college English teaching

Language testing is an important evaluation method in teaching evaluation. It can not only measure students' mastery of knowledge during the learning process, but also improve teaching methods and enhance teachers' teaching level. This article briefly analyzes the application of language testing in English teaching.
The Pandemic as Opportunity: Designing ‘New Normal’ Human Interactions

Can we design new, safe interactions for the pandemic world?
Reflections on the development of education and teaching of students in contemporary vocational colleges and universities

With the issuance of the Decision of the State Council on Accelerating the Development of Modern Vocational Education, which comprehensively deploys the accelerated development of modern vocational education, bringing new opportunities and challenges to the development of vocational education in China, the teaching of student education in contemporary vocational colleges and universities in the context of this guidance will reveal new features and face unprecedented challenges.
Domain-Invariant Feature and Generative Adversarial Network Boundary Enhancement for Multi-Source Unsupervised Hyperspectral Image Classification

Hyperspectral image (HIS) classification, a crucial component of remote sensing technology, is currently challenged by edge ambiguity and the complexities of multi-source domain data. An innovative multi-source unsupervised domain adaptive algorithm (MUDA) structure is proposed in this work to overcome these issues. Our approach incorporates a domain-invariant feature unfolding algorithm, which employs the Fourier transform and Maximum Mean Discrepancy (MMD) distance to maximize invariant feature dispersion. Furthermore, the proposed approach efficiently extracts intraclass and interclass invariant features. Additionally, a boundary-constrained adversarial network generates synthetic samples, reinforcing the source domain feature space boundary and enabling accurate target domain classification during the transfer process. Furthermore, comparative experiments on public benchmark datasets demonstrate the superior performance of our proposed methodology over existing techniques, offering an effective strategy for hyperspectral MUDA.
Cognitive Factors Affecting the Manufacturing Optimization Skills of Rural Indian BPO Workers

Crowdsourcing offers on-demand access to large numbers of human workers to implement new forms of human–computer collaborative functionalities that can be seamlessly integrated into advanced software and algorithms. However, crowdsourcing tasks are primarily undertaken by urban rather than rural workers. To enable the development of skilled rural employment, this research aims to assess rural crowdsourcing workers’ spatial reasoning and creative abilities and their abilities to solve irregular strip packing problems associated with the manufacture of sheet materials. The study conducted experiments and data collection with 140 rural Business Processing Outsourcing (BPO) workers located in six states of India. The statistical analyses of the data collected from seven rural BPO firms (140 rural workers) reveal that rural workers can achieve a 2D packing efficiency that is up to 8% higher than that of commercial algorithm outcomes. The results suggest that rural crowdsourcing can lead to effective job creation, skill development, and, for a modest cost, it can support industries that employ CAD/CAM systems to generate geometric data for common manufacturing processes.
Frasa Posposisional Bahasa Mooi

Conducting a research focusing on local language is one effort in conserving nation’s cultural values. The so-called Mooi Language is one of the Papuan’s local languages spoken in the district of West Sentani, Jayapura Regency, Papua. It is specifically used by the people living in the village of Maribu, Waibron, Kendate, and Sabron Dosay. The Mooi speakers are counted around 1,200 people. Mooi Language is included into the family of non-Austronesian languages with its order of sentence structure is subject-object-predicator (SOP). This research is conducted with qualitative approach using descriptive method. The aim of this study is to describe form and type of postpositional phrase found in the language of Mooi. The research is organized into three strategic stages. They are collecting, analyzing and serving the result of data analysis. The stage of collecting data in this research is carried out with conversational method between the researcher and Mooi Language speakers, in this case, who act as informants. Instruments used in this research are list of postpositional phrases and list of sentences containing form and type of postposional phrase. Based on the analysis, types of postpostional phrase in Mooi Language are categorized into (1) postpositional phrase signaling functional relation, (2) postpositional phrase signaling relation of origin and direction of place, (3) postpositional phrase signaling relation of partitipation, manner or equipment, (4) postpositional phrase signaling relation of place, (5) postpositional phrase signaling relation of direction to certain place, (6) postpositional phrase signaling relation of likeliness, and (7) postpositional phrase signaling relation of target/goal.
Kepribadian Tokoh Utama dalam Novel "Hidup ini Brengsek dan Aku Dipaksa Menikmatinya" karya Puthut E. A. vs Gindring Wasted

In life, one's problems will always arise both internally and externally. This study picks up Puthut's E.A. vs. Gindring Wasted novel titled Life Is a Jerk, and I'm Forced to Enjoy It. The approach used is literary psychology. Research methods are qualitative descriptive. The source of the data is the novel Life Is A Jerk, and I'm Forced to Enjoy It. Research data is in the form of phrases or sentences from the character "I" in the novel Life Is Jerk, and I Am Forced to Enjoy It. Data analysis uses Freud's personality theory (Id, Ego, and Super Ego), Lickona theory (Moral knowledge, moral feelings, and moral behavior), an interactive analysis model from Miles and Huberman. The results of this study are, 1) The personality of the main character has an honest personality, vindictive, does not want to filter the various offers he gets first, cowardly, lazy to mince words, and likes to forgive. 2) The educational value that can be taken from the novel Life Is Jerk, and I Am Forced to Enjoy It Puthut E.A. vs Gindring Wasted's work that is, obedient, full of sympathy, and the habit to do deeds according to the values and norms that apply by not judging someone.
The Potential of Lakes for Extracting Renewable Energy—A Case Study of Brates Lake in the South-East of Europe

The aim of this work is to provide some details regarding the energy potential of the local wind and solar resources near the Galati area (south-east of Romania) by considering the performances of a few recent technologies. Based on 22 years of ERA5 data (2001–2022), a picture concerning the renewable energy resources in the Brates Lake area is provided. Comparing the wind and solar resources with in situ and satellite data, a relatively good agreement was found, especially in regards to the average values. In terms of wind speed conditions at a hub height of 100 m, we can expect a maximum value of 19.28 m/s during the winter time, while for the solar irradiance the energy level can reach up to 932 W/m2 during the summer season. Several generators of 2 MW were considered for evaluation, for which a state-of-the-art system of 6.2 MW was also added. The expected capacity factor of the turbines is in the range of (11.71–21.23)%, with better performances being expected from the Gamesa G90 generator. As a next step, several floating solar units were considered in order to simulate large-scale solar projects that may cover between 10 and 40% of the Brates Lake surface. The amount of the evaporated water saved by these solar panels was also considered, being estimated that the water demand of at least 3.42 km2 of the agricultural areas can be covered on an annual scale.
Interval Type-2 Fuzzy PID Controller Using Disassembled Gradational Optimization

This paper presents an interval type-2 fuzzy proportional-integral-derivative (IT2F-PID) controller that is designed using a new disassembled gradational optimization (D-GO) method. A PID controller is first optimized using the D-GO method and then connected to a type-1 fuzzy logic system (T1-FLS). The parameters of the T1-FLS are optimized, and the T1-FLS is blurred into the interval type-2 fuzzy logic system (IT2-FLS). Finally, the IT2F-PID controller is formed. The proposed method is compared with the concurrent and general optimization methods. The simulation results show that the D-GO method reduces the optimization time by over 90% compared with the general method, and decreases the integral-of-time-absolute-error (ITAE) by 30%. Beyond that, compared with the concurrent optimization method, the D-GO method reduces time by over 25%, and the ITAE value by about 95%. In the normal case, model uncertainty, target uncertainty, and external disturbance, the control ability of the IT2F-PID controller designed using the D-GO method is verified via simulations using a nonlinear forced closed-loop system. The results show that the overshoot is reduced by 80% and the fluctuation is reduced by 67% compared with a traditional PID controller and an IT2F-PID controller built using the general method.
Geo-environmental testing

: Abstract Under current UK legislation and planning regulations the assessment of a site has to address potential contamination and associated environmental risks and waste management issues. Typically this will involve a qualitative and/or quantitative risk assessment which requires collection and interpretation of appropriate geo-environmental data and relevant environmental surveys. Data are required relating to contamination sources, including the chemical composition of soils, groundwater and soil gas, in addition to potential receptors which may be impacted. The quality of this data depends on the use of good practice in the selection, collection, handling, storage and analysis of samples. The value of the risk assessment further depends on the processing and interpretation of the data. In the context of a ground investigation, there are significant benefits to combining the collection of geo-environmental data and geotechnical requirements, provided it is done in an appropriate and targeted manner.