Reviewerly/exHarmony huggingface dataset:
The Creative Sector of the Innovative Educational Environment Among Universities of Culture

The article substantiates the need to develop new methodological approaches to the study of creative industries, the development of which in the conditions of digitalization in higher education institutions of culture and art occupies a significant place. The authors outline the range of problems in order to bring them out of the category of secondary problems, focusing the attention of the scientific community on innovations in universities of culture that change the perception of their role in the socio-economic development of territories. The focus on innovative processes and active implementation of new digital technologies forms the basis for the creation of a modern educational ecosystem. The emergence of such systems involves critical reflection on the effectiveness of scientific and technological changes and unintended consequences for society, as well as the analysis of successful practices and positive experience. Using Kazan State Institute of Culture as an example, the authors describe the specifics of creating and operating an Engineering Center, a Prototyping Centre, the emergence of business start-ups, and research in creative higher education in general. The prospects of participation of educational ecosystems in the modernization of the region are assessed as positive, receiving socio-economic support that meets the strategic objectives of territorial development. According to the authors, under the conditions of digitalization, only in combination with the traditional practices of cultural, leisure, performing, and library activities, which are carefully protected and reproduced in the process of education in universities, is it possible to implement the strategy of the state cultural policy of Russia as a condition for the preservation of the diversity of its original culture and civilizational identity. The conclusion is made about a fundamentally new qualification passport for a graduate of a university of culture and art, who is ready to meet the challenges of the digital age, which increases his/her competitiveness in the labour market.
Dynamic Cost Estimation of Reconstruction Project Based on Particle Swarm Optimization Algorithm

In order to predict the value of dynamic cost estimation of reconstruction project, this paper proposes the research on dynamic cost estimation of reconstruction project based on particle swarm optimization algorithm. Firstly, the applicability of example swarm optimization algorithm is introduced. The basic principle of particle swarm optimization algorithm is described, and PSO (particle swarm optimization) algorithm is used to optimize the super parameters of LS- SVM. With the help of spss20 0 to cluster the sample data to obtain similar engineering classes. In order to better verify the application effect of the optimized model in project cost prediction, BP neural network, LS - SVM and PSO - LSSVM are selected to simulate and predict the project cost. The results show that the relative errors of the three models are controlled within + 10%, which can meet the accuracy requirements of construction cost prediction in the early stage of construction. The relative error distribution interval predicted based on BP neural network model is [- 7.46%, 5.74%], and its range is 13.12%; The relative error distribution interval predicted based on LS SVM model is [- 8.12%, 6.17%], and its range is 14.22%; The relative error distribution interval predicted based on PSO -LSSVM model is [- 2.56%, 2.49%], and its range is 5.21%. The prediction model optimized by PSO algorithm is better than LS SVM model in prediction stability, and the prediction effect is more robust. In conclusion, the prediction model based on PSO optimized LS SVM has good guiding significance for the construction cost, and is more suitable for the prediction of the construction cost in the early stage of construction.
Prediction and Anomaly Detection of accelerated particles in PIC simulations using neural networks

Acceleration processes that occur in astrophysical plasmas produce cosmic rays that are observed on Earth. To study particle acceleration, fully-kinetic particle-in-cell (PIC) simulations are often used as they can unveil the microphysics of energization processes. Tracing of individual particles in PIC simulations is particularly useful in this regard. However, by-eye inspection of particle trajectories includes a high level of bias and uncertainty in pinpointing specific acceleration mechanisms that affect particles. Here we present a new approach that uses neural networks to aid individual particle data analysis. We demonstrate this approach on the test data that consists of 252,000 electrons which have been traced in a PIC simulation of a non-relativistic high Mach number perpendicular shock, in which we observe the two-stream electrostatic Buneman instability to pre-accelerate a portion of electrons to nonthermal energies. We perform classification, regression and anomaly detection by using a Convolutional Neural Network. We show that regardless of how noisy and imbalanced the datasets are, the regression and classification are able to predict the final energies of particles with high accuracy, whereas anomaly detection is able to discern between energetic and non-energetic particles. The methodology proposed may considerably simplify particle classification in large-scale PIC and also hybrid kinetic simulations.
Disparate Impact in Facial Recognition Stems from the Broad Homogeneity Effect: A Case Study and Method to Resolve

Automated face recognition algorithms generate encodings of face images that are compared to other encodings to compute a similarity score between the two originating face images. These face encodings, also known as feature vectors, contain representations of various facial features. Some of these facial features, but not all, have been shown to resemble each other across different subjects that happen to share a demographic group assignment, such as having the same race or gender. Recent work has shown that these demographically dependent features can increase similarity scores between different individuals who belong to the same demographic group compared to similarity scores for different individuals in different groups. When one feature vector is compared to many other feature vectors, as in identifications, this effect, referred to as “demographic clustering”, can lead to un-equal false positive identification error rates for different demographic groups. In this study, we propose a method of mitigating this clustering effect from face recognition algorithms to reduce these un-equal error outcomes. Our method presumes that feature space patterns shared within demographic groups can be removed while preserving other distinct features of individuals. In this paper, we prove that this is possible, in principle, by applying linear dimensionality techniques to the feature space of two ArcFace face recognition algorithms. We show this method increases four distinct “fairness” measures while preserving useful true match rates.
Wink Lens Smart Glasses in Communication Engineering: Catalyst for Metaverse and Future Growth Point

Meta-universe is a very hot concept recently in the field of communication engineering, which is a virtual world where people can interact and communicate using VR/AR and other means.Since XR headset is one of the hardware that is crucial to the metaverse for consumers, the rise of metaverse is likely to be the main catalyst for the growth of XR headset shipments in the coming years.Smart glasses are one of them and one of the most promising wearable smart devices in the field of communication engineering.Smart glasses are similar to smartphones with autonomous operating systems that can be installed with software to achieve various functions, such as adding schedules, map navigation, interacting with friends, taking photos and videos, making video calls, etc., and enabling wireless network access through mobile communication networks.With the development of communication technology, smart glasses are also gradually connected with other devices, such as cell phones and tablet PCs, which can share and transmit information through Bluetooth, WI-FI and other communication technologies.In addition, in the field of smart glasses, there are many key technologies that need continuous innovation and breakthrough, such as binocular stereo display technology, human-machine interaction technology, voice recognition technology, etc.The continuous improvement of these technologies will further promote the development of smart glasses.
Unveiling Linguistic Harmony: Asserting Interlingual Synchronicity in Documents

Publishing texts in multiple languages always involves issues of coordination of both content and structure. The Swiss Chancellery must deal with documents in four official languages, and they face special problems in the production of compilations that merge amendments into existing legislation. Although the source documents are prepared in MS Word, they are issued in derivative products such as PDF, HTML, and Akoma Ntoso. To aid coordination between versions, the authors are developing a reporting system that does rule-driven sanity checks on parallel documents, starting with the XML of Akoma Ntoso. Each rule is represented by XSLT processes, and the selection of rules to be applied is controlled by XProc, with the results displayed as messages interpolated into an HTML rendition of the source document.
Effects of error rates and target sizes on neck and shoulder biomechanical loads during augmented reality interactions

Augmented reality (AR) interactions have been associated with increased biomechanical loads on the neck and shoulders. To provide a better understanding of the factors that may impact such biomechanical loads, this repeated-measures laboratory study evaluated the effects of error rates and target sizes on neck and shoulder biomechanical loads during two standardized AR tasks (omni-directional pointing and cube placing). Twenty participants performed the two AR tasks with different error rates and target sizes. During the tasks, angles, moments, and muscle activity in the neck and shoulders were measured. The results showed that the target sizes and error rates significantly affected angles, moments, and muscle activity in the neck and shoulder regions. Specifically, the presence of errors increased neck extension, shoulder flexion angles and associated moments. Muscle activity in the neck (splenius capitis) and shoulder (anterior and medial deltoids) also increased when the errors were introduced. Moreover, interacting with larger targets resulted in greater neck extension moments and shoulder abduction angles along with higher muscle activity in the splenius capitis and upper trapezius muscles. These findings indicate the importance of reducing errors and incorporating appropriate target sizes in the AR interfaces to minimize risks of musculoskeletal discomfort and injuries in the neck and shoulders.
A Design and Build a Website for Catering Sales and Orders Using Web Engineering

Ordering catering is an activity carried out by consumers to order products in the form of food packages. At this time catering orders in the Garut area are still done manually, consumers see a list of products through brochures, even though consumers do not necessarily know the shape of the product written on the brochure, it will make it difficult for consumers to determine which product to choose, and these difficulties will making consumers have to meet with the owner of the catering frequently which will hinder time and drain costs. The existence of technology encourages researchers to build a catering ordering website which is expected to facilitate consumers in the catering ordering process. Consumers can find out the list as well as pictures of the products provided, with that consumers can easily determine which product to choose. Consumers can also save time and costs, because there is no need to repeatedly meet with the catering owner to find out the details of the products provided. The method used in the development of this application is the Web Engineering method with the stages of communication, planning, modeling, and deployment, using the php, html programming languages, using the Codeigniter framework and bootstrap and with MySql database management, by testing the Black Box Testing application.
CauseFormer: Interpretable Anomaly Detection With Stepwise Attention for Cloud Service

The anomaly detection techniques for cloud service focus on alerting the operation engineers about the anomalous running state. However, their shortcoming of anomaly interpretability is an obstacle to understanding and further removing the anomalies. To overcome the abovementioned challenge, we propose a tree-like attention-based detection framework CauseFormer that provides both the metric and sample interpretations. Firstly, we develop stepwise attention based on the multi-head attention mechanism, which imitates the rule-based tree formation process. This network block extracts the higher-order features and generates the metric contribution that can be regarded as metric interpretation. Meanwhile, we design the hyper-circle loss function rather than cross-entropy-based approaches to optimize the representation. Then we introduce the majority voting rule into the classifier. This neighbor classification criterion raises the alarms of anomalies and achieves the sample interpretation. Finally, we conduct extensive experiments in four datasets collected from cloud application cases. The experimental results reveal the superiority of CauseFormer in improving detection accuracy and embodying practical interpretability.
A Workflow Allocation Strategy Using Elitist Teaching–Learning-Based Optimization Algorithm in Cloud Computing

Cloud computing offers a pay-per-use basis to address complex scientific and commercial workflow processing. This paper proposes an elitist teaching–learning-based optimization (E-TLBO) by replacing the worst candidates with the elitist candidate of the previous population to minimize the makespan of workflow tasks submitted from cloud users. This metaheuristic method mimics the classroom teaching and learning behaviors. The workflow allocator is implemented in MATLAB for the performance analysis. The experimental findings demonstrate the better performance than its peer in terms of fitness value (makespan) and convergence rate under study.
Human Face Detector with Gender Identification by Split-Based Inception Block and Regulated Attention Module

Smart digital advertising platforms have been widely arising. These platforms require a human face detector with gender identification to assist them in the determination of providing relevant advertisements. The detector is also prosecuted to identify the gender of a masked face in post-coronavirus situations and demanded to operate on a CPU device to lower system expenses. This work presents a lightweight Convolution Neural Network (CNN) architecture to build a gender identification integrated with face detection to respond to these issues. This work proposes a split-based inception block to efficiently extract features at various sizes by partially applying different convolution kernel sizes, levels, and regulated attention module to improve the quality of the feature map. It produces slight parameters that drive the architecture efficiency and can operate quickly in real-time. To validate the performance of the proposed architecture, UTKFace and Labeled Faces in the Wild (LFW) datasets, modified with an artificial mask, are utilized as training and validation datasets. This offered architecture is compared to different lightweight and deep architectures. Regarding the experiment results, the proposed architecture outperforms masked face gender identification on the two datasets. In addition, the proposed architecture, which integrates with face detection to become a human face detector with gender identification can run 135 frames per second in real-time on a CPU configuration.
A Style-Based Caricature Generator

A facial caricature is a creation of new artistic and exaggerated faces which translates into a real image to convey sarcasm or humor while keeping the identity of the subject. In this work, we proposed a new way to create caricatures by exaggerating facial features like the eyes and mouth while keeping the facial contour intact and a realistic style. Our method can be categorized into two steps. First, the facial exaggeration process transformed faces into caricature face images while maintaining facial contours. Second, the appearance style generator is trained in unpaired using the generated caricature faces to produce a facial caricature that can change to any realistic style of our preference. Experimental results show our model produces more realistic and disentangled caricature images as compared to some of the previous methods. Our method can also generate caricature images from real images.
The digital competence of Chinese higher education students and the linkage with their career adaptability

Purpose Recently, digital competence has become one of the most important work competencies of employees. This study investigated students' digital competence in the context of Chinese higher education and examined digital competence's relationship with students' career adaptability. Design/methodology/approach The participants were 298 Chinese higher education students from both universities and colleges. MANOVA (Multivariate Analysis of Variance) was carried out through SPSS (Statistical Package for the Social Sciences) 26.0 to examine whether digital competence varied with the students' personal characteristics, i.e. gender, grade level and school type. In addition, the structural equation model was employed with Mplus 8.3 to analyse the relationship between digital competence and career adaptability as well as the mediation role of digital informal learning and academic performance. Findings The descriptive statistics revealed that students performed best in the safety construct, whilst worst in the digital content creation construct. The result of MANOVA showed that the digital competence of the participants significantly varied with participants' gender, school type and grade level. Furthermore, the structural equation model results demonstrated that higher education students' digital competence was positively associated with the students' career adaptability. And the mediation role of digital informal learning and academic performance was also confirmed. Originality/value To the authors' knowledge, this is the first study focussing on the relationship between digital competence and career adaptability. In particular, both working and learning perspectives were considered to explore this relationship. Besides, the authors also displayed Chinese higher education students' digital competence with consideration of different districts and school types.
Cherry-Picking with Reinforcement Learning

Grasping small objects surrounded by unstable or non-rigid material plays a crucial role in applications such as surgery, harvesting, construction, disaster recovery, and assisted feeding.This task is especially difficult when fine manipulation is required in the presence of sensor noise and perception errors; errors inevitably trigger dynamic motion, which is challenging to model precisely.Circumventing the difficulty to build accurate models for contacts and dynamics, data-driven methods like reinforcement learning (RL) can optimize task performance via trial and error, reducing the need for accurate models of contacts and dynamics.Applying RL methods to real robots, however, has been hindered by factors such as prohibitively high sample complexity or the high training infrastructure cost for providing resets on hardware.This work presents CherryBot, an RL system that uses chopsticks for fine manipulation that surpasses human reactiveness for some dynamic grasping tasks.By integrating imprecise simulators, suboptimal demonstrations and external state estimation, we study how to make a realworld robot learning system sample efficient and general while reducing the human effort required for supervision.Our system shows continual improvement through 30 minutes of real-world interaction: through reactive retry, it achieves an almost 100% success rate on the demanding task of using chopsticks to grasp small objects swinging in the air.We demonstrate the reactiveness, robustness and generalizability of CherryBot to varying object shapes and dynamics (e.g., external disturbances like wind and human perturbations).Videos are available at https://goodcherrybot.github.io/
Time Optimal Ergodic Search

Robots with the ability to balance time against the thoroughness of search have the potential to provide time-critical assistance in applications such as search and rescue.Current advances in ergodic coverage-based search methods have enabled robots to completely explore and search an area in a fixed amount of time.However, optimizing time against the quality of autonomous ergodic search has yet to be demonstrated.In this paper, we investigate solutions to the time-optimal ergodic search problem for fast and adaptive robotic search and exploration.We pose the problem as a minimum time problem with an ergodic inequality constraint whose upper bound regulates and balances the granularity of search against time.Solutions to the problem are presented analytically using Pontryagin's conditions of optimality and demonstrated numerically through a direct transcription optimization approach.We show the efficacy of the approach in generating time-optimal ergodic search trajectories in simulation and with drone experiments in a cluttered environment.Obstacle avoidance is shown to be readily integrated into our formulation, and we perform ablation studies that investigate parameter dependence on optimized time and trajectory sensitivity for search.
Generative AI with Personalities

This chapter presents a human-centered design tool called the persona, which you can apply to great effect when you prompt a generative AI. The objective is twofold. The first is to bring attention to various AI personas that have migrated from science fiction to describe narrow and general AI. The second is to highlight a feature of generative AI that can be creatively prompted to embody personalities, characters, and emotions informing the content that it generates. That content can provide momentum for characters you may want to develop in your own creative work.
Definitions and (Uniform) Interpolants in First-Order Modal Logic

We first consider two decidable fragments of quantified modal logic S5: the one-variable fragment and its extension S5ALC that combines S5 and the description logic ALC with the universal role. As neither of them enjoys Craig interpolation or projective Beth definability, the existence of interpolants and explicit definitions of predicates---which is crucial in many knowledge engineering tasks---does not directly reduce to entailment. Our concern therefore is the computational complexity of deciding whether (uniform) interpolants and definitions exist for given input formulas, signatures and ontologies. We prove that interpolant and definition existence in the one-variable fragment of quantified modal logic S5 and in S5ALC is decidable in coN2ExpTime, being 2ExpTime-hard, while uniform interpolant existence is undecidable. Then we show that interpolant and definition existence in the one-variable fragment of quantified modal logic K is nonelementary decidable, while uniform interpolant existence is undecidable.