Reviewerly/exHarmony huggingface dataset:
Storytelling in the Metaverse: From Desktop to Immersive Virtual Reality Storyboarding

Creatives from the animation and film industries have always been experimenting with innovative tools and methodologies to improve the creation of prototypes of their visual sequences before bringing them to life. In recent years, as realistic real-time rendering techniques have emerged, the increasing popularity of virtual reality (VR) can lead to new approaches and solutions, leveraging the immersive and interactive features provided by 3D immersive experiences. A 3D desktop application and a novel storyboarding pipeline, which can automatically generate a storyboard including camera details and a textual description of the actions performed in three-dimensional environments, have already been investigated in previous work. The aim was to exploit new technologies to improve existing 3D storytelling approaches, thus providing a software solution for expert and novice storyboarders. This research investigates 3D storyboarding in immersive virtual reality (IVR) to move toward a new storyboarding paradigm. IVR systems provide peculiarities such as body-controlled exploration of the 3D scene and a head-dependant camera view that can extend features of traditional storyboarding tools. The proposed system enables users to set up the virtual stage, adding elements to the scene and exploring the environment as they build it. After that, users can select the available characters or the camera, control them in first person, position them in the scene, and perform actions selecting from a list of options, each paired with a corresponding animation. Relying on the concept of state-machine, the system can automatically generate the list of available actions depending on the context. Finally, the descriptions for each storyboard panel are automatically generated based on the history of activities performed. The proposed application maintains all the functionalities of the desktop version and can be effectively used to create storyboards in immersive virtual environments.
The EXPERIENCE Project: Unveiling Extended-Personal Reality Through Automated VR Environments and Explainable Artificial Intelligence

Virtual reality (VR) technology offers immersive experiences, but its widespread adoption is hindered by technical barriers and a lack of consideration for users' physiological changes. This paper provides an overview of the EXPERIENCE system, which addresses these challenges by enabling individuals to create VR environments using portable devices without technical expertise. The project introduces Extended-Personal Reality (EPR), capturing users' subjective experiences through physiological responses, psychological descriptors, and behavioral outcomes. The comprehensive EPR environment allows for personalized exchanges of psychological and emotional responses. The integration of explainable AI, multisensory biofeedback, and individualized perceptions of time and space enables dynamic calibration and manipulation of the VR environment. The project focuses on minimizing psychological distance, particularly in temporal and spatial perception, to enhance user experiences. The EXPERIENCE technology holds significant potential in the healthcare of mood disorders, as well as gaming, e-learning, and neuroeconomics, offering a user-friendly platform for personalized virtual experiences and potentially creating a new market for unique virtual worlds tailored to individuals' psychological and emotional states.
VO.I.C.E. FIRST: Supporting Human Assistants with Real-Time Voice Understanding

While AI and automation have made significant strides in customer support, there are still situations where human intervention via voice channels is necessary to provide the best possible customer experience. In fact, although AI and chatbots have become increasingly sophisticated, they may not always be able to handle complex or nuanced customer issues. Human agents can better understand and respond to these situations, providing tailored solutions. At the same time, solving non-trivial customer problems often requires access to knowledge bases and contextual customer information, for which AI is particularly well suited. Hence the idea of integrating human and artificial intelligence in a hybrid solution. We developed an AI system to help human assistants in the process of handling conversations. This system can be viewed as a collaborative bot (cobot). The cobot captures the audio stream of the conversation, converts it to text and analyzes it in real time. The extracted tokens are classified and sent to a reasoning system based on a knowledge graph, that provides information and action suggestions to the human assistant. Assistants are also capable of providing information to the reasoning system, utilizing their human understanding of the client's circumstances as they unfold. While designing a prototypical solution for utility services, we have faced the problem of real-time use of computationally complex procedures, including spontaneous speech understanding and knowledge-based heuristic rules. Moreover, we adopted a standards-based approach and experimented with open source reasoners and publicly available language models. The paper outlines the system architecture and design, and discusses the results of the first experiments.
Neural Networks Embedded in Wearable Devices: a Preliminary Digital vs. Analog Comparison

A fully connected artificial neural network (NN) for color identification (with 125 neurons and 11 color classes with inputs from an RGB sensor) was developed to study the challenges of real-time, low-power and low-latency machine learning to be embedded in wearable devices. Digital and analog approaches were compared in terms of miniaturization, power consumption, accuracy and speed. A prototype was built using a Nordic nRF52840 microcontroller with BLE, where the NN runs with an energy consumption of 6 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> J/class and sub-ms time. The feasibility of an alternative analog implementation of the NN in a dedicated integrated circuit by means of switched capacitors was evaluated through simulations, focusing in particular on the impact of weights quantization and the potentially significant saving in terms of energy dissipation (2 nJ/class),
The Implementation of a Mobile Game for Social Inclusion in Multicultural School Contexts

This paper introduces a serious mobile game designed to promote the social inclusion of children from migrant backgrounds. By focusing on the development of empathic skills in native children at primary school, the game aims to bridge the gap between diverse groups and promote harmonious interactions. The game features two different gameplay scenarios, each of them consisting of 6 levels, which presuppose the acquisition of increasingly complex skills based on the Social-Emotional Learning (SEL) framework. The identified learning goals range from the identification of others' emotions based on expressive and social cues, passing through gathering information and understanding of cultural diversity, to team working and collaborative problem- solving. Through the engagement of players with the experiences of peers from diverse cultural backgrounds, the game provides a unique opportunity to gain a deeper understanding of the challenges faced by children with migrant backgrounds. By providing students with a safe and interactive learning environment that promotes SEL skills, the current game has the potential to positively impact the classroom climate and promote cooperative learning among students.
TinyRCE: Forward Learning Under Tiny Constraints

The challenge posed by on-tiny-devices learning targeting ultra-low power devices has recently attracted several machine learning researchers. A typical on-device model learning session processes real time streams of data produced by heterogeneous sensors. In such a context, this paper proposes TinyRCE, a forward-only learning approach based on a hyperspherical classifier aiming to be deployed on microcontrollers and, potentially, on sensors. The learning process is fed by labeled data streams to be classified by the proposed method. The classical RCE algorithm has been modified by adding a forget mechanism to discard useless neurons from the classifier's hidden layer, since they could become redundant over time. TinyRCE is fed with compact features extracted by a convolutional neural network which could be an extreme learning machine. In such case, the weights of the topology were randomly initialized instead of trained offline with backpropagation. Its weights are stored in a tiny read-only memory of 76.45KiB. The classifier required up to 40.26KiB of RAM to perform a complete on-device learning workload in 0.216s, running on an MCU clocked at 480MHz. TinyRCE has been evaluated with a new interleaved learning and testing protocol to mimic an on-tiny-device forward learning workload. It has been tested with openly available datasets representing human activity monitoring (PAMAP2, SHL) and ball-bearing anomaly detection (CWRU) case studies. Experiments have shown that TinyRCE performed competitively against a supervised convolutional topology followed by a SoftMax classifier trained with backpropagation on all these datasets.
Analysis and Mitigation of the Imbalance Impact on an Industrial Image Classification Dataset

One of the most impacting problems in building machine and deep learning models for automated vision systems in industrial environments is the large variety of products in a real production line, which means that the dataset generated from the systems will be, most probably, imbalanced. Different approaches have been studied in the recent literature to reduce the effects of imbalanced data, still without a recommendation of a more adequate methodology for industrial scenarios. Hence, this paper compares three approaches for reducing the effects of imbalanced classes using a dataset of real images collected in an industrial production line: class removal, weight compensation, and data augmentation. We use a convolutional neural network as the backbone for the classifier of proposed method. Several comparisons are presented, emphasizing the advantages and limitations of each approach. Results show that data augmentation is the most promising approach for the evaluated dataset, improving the results and allowing the real-world application of the proposed method.
The Role of Motivation in Russian Heritage Language Learner Performance

Heritage language learning is an interesting phenomenon that affects students of any heritage language. In this paper, I am putting my focus directly on Russian heritage language learners. Through research on pedagogical learning theories, students’ motivation to take heritage language courses, and current Russian heritage language learning studies, I am exploring the impact that motivation has on Russian heritage language learners’ performance in these specific courses, and some of the problems these students encounter in the classroom. This paper highlights the connections between motivations in learning and the specific problems Russian heritage language learners struggle with in the classroom, and offers suggestions towards improving HLL motivation.
The concept of educational services in Russian legislation

The article examines the concept of “educational service” in the context of the public discussion that has arisen about the possibility of its use in relation to education and educational relations. The current situation with the gradual abandonment of the term in the current educational legislation is considered, which corresponds to the provisions of the new version of the Constitution of the Russian Federation and the policy in the field of traditional spiritual and moral values, but is not an effective enough measure to solve all emerging problems in the segment of legal regulation of higher education. The analysis of the existing educational relations was carried out, as a result of which it was concluded that education and educational services are not identical, the first is one of the most significant human rights and one of the largest public institutions, the second is rather a technical tool for describing financial and civil law relations related to the realization of everyone’s right to education. The author examines the problems with the use of the term “educational service” in the current legislation, as well as the difficulties arising in this regard in the course of law enforcement. The author pays special attention to the further improvement of the legal regulation of educational relations in this part.
Restructuring the Software Architecture: A Case Study of the CoolBiz Core Banking Platform

As the structural engineering underpins the resilience of a city built on an active geological fault, software architecture becomes crucial in an increasingly digital society. This paper investigates the challenges of rigid, low cohesion software structures through a detailed case study of the CoolBiz Platform, an integrated Core Banking solution. The platform currently faces significant issues in its service support framework, including low flexibility, unsatisfactory cohesion, non-adherence to SOLID principles, absence of unit tests, and lack of documentation. This study aims to describe the planning and implementation of a new event-driven architecture for the CoolBiz Platform. This architecture is expected to not only resolve current technical challenges but also bring significant business benefits, such as the implementation of language agnosticism, a strategy aimed at facilitating talent recruitment and retention by not limiting recruitment to expertise in a specific programming language.
Utilization of Podcasts as Supporting Media for the Digitalization of Kumkm-Tangguh Schools in Batu City

The Resilient CMSMEs School Program is a mentoring and training program which is one of the services offered by PLUT-CMSMEs Kota Batu. However, the benefits of this program have not been widely accepted by CMSMEs. Digitalization efforts by utilizing digital platforms are expected to be able to expand the reach of the Resilient CMSMEs School. In addition, digital platforms such as podcasts can be used as media for introducing and marketing MSME products in Batu City. The Universitas Brawijaya’s MF Team is proactive in initiating and assisting Resilient CMSMEs School Digitalization activities. The purpose of this activity is to conduct (1) analyze the requirement for digitalization of Resilient CMSMEs School Program, (2) procure podcast development facilities and (3) assist in developing podcast content. Activities carried out for 6 months, which consists of 4 stages, namely (1) CMSME survey, (2) procurement of podcast tools, (3) implementation of making podcasts and (4) evaluation of activities. Based on the results of the CMSME survey, the MF team plans activities and digital content; procures podcast equipment and provides assistance in developing podcast content. Evaluation of activities is carried out at the end of the activity based on the number of viewers and likes on the video podcast uploaded on YouTube.
Dynamic heuristic algorithm solves virtual resource inventory optimization problem in cloud computing environment

In recent years, the rapid advancement of deep learning technologies has brought forth innovative solutions for inventory optimization. This paper addresses the issue of virtual resource inventory optimization, with a primary focus on achieving optimal service levels and inventory turnover in the context of cloud computing. It is worth noting that we consider the unique characteristic of virtual resources, which can be dynamically released. We propose a novel dynamic heuristic algorithm to formulate inventory strategies for each virtual resource at different decision points, aiming to achieve an optimal balance between service levels and inventory turnover. This paper provides valuable insights into the effective optimization of virtual resource inventory and offers practical guidance for future endeavors in this field.
A Survey of Transformers in Video Prediction

Transformer is an encoder-decoder architecture based on the self-attention mechanism, which can effectively obtain global information and has shown great strength in the construction of long-distance dependencies. In recent years, Transformer has become the mainstream architecture of Natural Language Processing (NLP). Inspired by Transformer's success in NLP, researchers have gradually applied it to video processing tasks, one of which is video prediction. The essence of video prediction tasks is to generate future frames based on past ones. The model in this task needs to have strong sequence modeling capabilities, so the application and fusion of Transformer has become a major direction. This paper sorts out the application of Transformer in video prediction, lists typical models and analyzes improvement ideas, and finally summarizes and looks forward to its development.
Distributed Storage Big Data Parallel Clustering Scheduling Algorithm Based on Decision Tree

To optimize the parallel clustering scheduling effect of distributed storage big data, this study proposes the design of a decision tree based parallel clustering scheduling algorithm for distributed storage big data. This method first constructs a decision tree model to achieve distributed storage data classification, and then implements parallel design of the decision tree model based on the Storm platform to improve data classification speed. Finally, based on this, a distributed storage data clustering scheduling design is implemented using an improved BPSO algorithm. The experimental results show that the proposed method has a shorter scheduling time and is superior to traditional methods, with better application results.
Research on Tobacco Industry Sales Forecast Based on Cloud Computing and SSA-SVR

The accuracy of supply forecast and sales forecast of tobacco industry is directly related to the production and development of tobacco industry. Supply and sales forecast is the premise and basis of production and sales plan, which can improve the scientific nature of production and sales plan, so as to create more economic benefits for the tobacco industry and promote social and economic development. The application of cloud computing technology and SSA-SVR hybrid model can effectively solve the problem of tobacco industry and improve the sales prediction accuracy of tobacco industry. In this regard, this paper combines the literature data method, case analysis method, statistical methods, comparative experiment and other methods to deeply study the tobacco industry sales forecast based on cloud computing and SSA-SVR.
BiLSTM Text Classification Incorporating Attentional Mechanisms

Most of the current research on text classification studies feature extraction or global information extraction at the surface level, which considers all features as important features, greatly increasing the model computational power. The existing text classification model bi-directional long short-term memory (BiLSTM) can learn text contextual information, but cannot be targeted to the extraction of important features and special attention. This paper incorporates the attention mechanism into the BiLSTM model, so that the model in the learning of contextual information, while the model This paper integrates the attention mechanism into the BiLSTM model, which makes the model learn contextual information while extracting locally important feature information, reduces the number of meaningless text features, and finally speeds up the convergence of the model, and ultimately achieves a higher accuracy of the classification results.
A Reinforcement Learning Based on Book Recommendation System

As the Information Age continues to evolve, the significance of recommendation systems in people's daily lives becomes increasingly prominent. Traditional recommendation algorithms, such as content-based filtering, matrix factorization, logistic regression, factorization machines, neural networks, and multi-armed bandits, predominantly focus on immediate feedback for recommended items, often overlooking long-term rewards. This paper aims to investigate the application of reinforcement learning in personalized book recommendation systems, with the objective of enhancing user experience and recommendation accuracy.
Synthesiser Sounds and Phenomena in China

Under the STS perspective, the thesis explores the cultural, regional and personal attributes of Chinese-made synthesizers ascribed by their designers and makers in various phases, the natural, social and transformative properties of the synthesizer's output sounds, and the changing conception of "synthesizer" as a cultural symbol in the Chinese ideology.
Integrating Big Data Intelligence into K-12 Schools Campus Safety Management

The safety of K-12 schools has always been a paramount concern. As society progresses and technology advances, the utilization of big data intelligence to innovate campus safety management has emerged as a significant area of research. This study identifies four prevalent issues in the current safety management practices of K-12 schools: a deficiency in safety education and awareness, limited capabilities for risk prediction and early warning, lagging emergency response systems, and ineffective inter-departmental communication. By applying big data intelligence, this paper proposes the development of four novel platforms for enhancing safety management in K-12 schools: a Machine Learning-based Safety Education Platform, a Big Data-based Risk Prediction and Warning Platform, an AI-based Monitoring and Response Platform and an IoT-based Safety Information Sharing Platform. These platforms aim to provide comprehensive safety management solutions encompassing real-time monitoring, early warning, incident response, in-event control, and post-incident traceability. The proposed multi-faceted approach promises to foster the improvement in K-12 schools campus safety management, thereby ensuring a safer and more comfortable learning environment.
DESIGN AND IMPLEMENTATION OF A 36-NOTE LOW-COST CUSTOMIZED DIGITAL MUSIC BOX

The purpose of the current study was to design and build a music box with digitally produced notes. The input command sheet allows anybody to create any melody from the octave, making it possible to think of it as a small version of a musical instrument. This instrument is capable of producing frequencies in all three octaves, i.e., the lower, the middle, and the higher octaves. This music box may therefore play any tune, no matter how short or lengthy the note is. There is no need to alter the frequencies over time because no string is used in the creation. It can play any song to one’s heart’s content if the song’s music sheet is accessible. In an epitome, this music box can be viewed as a very simple musical instrument with digitally created tones. There is no need to customize the strings over time like with traditional mechanical musical instruments owing to tones produced digitally. The ability to generate a vast variety of tones, the ease with which it can be adjusted, the convenience with which anyone may have their desired notes customized, and the low cost at which it was produced are the most notable advantages of this digital music instrument. Due to its low price, it can be simply incorporated, altered, slightly improved, and produced at a considerably reduced cost for commercial use.