Reviewerly/exHarmony huggingface dataset:
Proposal for the Implementation of a Web Platform to Reinforce the Learning of the Mathematical Reasoning Course for Sixth Grade of Primary School

Currently, the traditional teaching methodology used in educational centers is not effectively adapted to the current technological era, which can lead to poor academic performance of students, especially in the subject of mathematical reasoning. The objective of the research is to develop a web platform that reinforces the learning of mathematical reasoning in sixth grade students. For this purpose, the agile software development methodology SCRUM was used, due to its agility and flexibility in the development process. In addition, the Game Learning methodology was used for the development of the web page content. The results obtained by conducting the experiment through a control and experimental group, in addition to the application of a survey, obtained a Spearman’s Rho of 0.891 and a significance of less than 0.05, demonstrating that there is a relationship between the use of the web platform and the learning of mathematical reasoning. Thus, it is demonstrated that the use of ICT (information and communication technologies) benefits students, since it improves in several aspects in relation to traditional teaching, as evidenced in the experiment an improvement of 3.331% of the experimental group compared to the control group. Therefore, it is plausible to conclude that the use of a web platform accompanied by the Game Learning methodology reinforces students’ learning in general.
Mobile application for interactive learning of human anatomy using augmented reality

In the last decade, the push for digitalization due to the recent pandemic has led to the constant evolution of different fields. However, in the field of anatomy education, students' learning resources remain the same as they have been used in recent years, as does the lack of technological skills among educators in Peru. With the aim of providing a new alternative for learning human anatomy to the general public and promoting technology, the project aims to design, implement and validate a mobile application solution using augmented reality. This innovative approach helps to address a potential impact on the quality of education in the country. Our research demonstrates the feasibility and benefits of integrating AR into anatomy education such as improved enthusiasm for learning and retention of information versus traditional methods. All of this is reflected in the enhancement of comprehension and educational outcomes.
A system for detecting objects and estimating their distance using a neural network

This article proposes using neural networks to solve the challenge of accurately measuring the distance of an object using cameras and digital image processing. For this, a neural network was trained using a data set that includes information on the distance in pixels of the centers of mass of the object detected by the cameras. This data was used to teach the network to make an accurate estimate of the actual distance of the object. Image analysis methods were also used in conjunction with images of the object previously captured and trained with YoloV8 on Roboflow. The results obtained showed a notable improvement in the precision that is obtained when measuring the distance without the tedious calibration that is had in the other approaches considered for this investigation. Overcame the challenges associated with camera calibration due to possible distortion, accuracy, and generalization generated by changing the environment, resulting in an effective solution with 90% accuracy percentage and a dense neural network with an input layer, a hidden layer and an output layer with 2000 training cycles. These results demonstrate the potential of neural networks and image processing to address distance measurement problems in various applications, such as robotics, road safety, and autonomous navigation.
Communication of Earthquake Warning Systems Through the RDS Standard Using the CAP-PER Protocol in Building Health Monitoring

The alert dissemination time of a seismic alert system allows evaluating the efficiency in alerting the population in a short time. In this research, a seismic alert system was proposed to generate an alert message automatically according to the vibration level of the P wave in a building when an earthquake occurs. This system is composed to an Internet-FM communication system, servers and acquisition and activation modules. The results show that the seismic alert system has a rapid response, less than or equal to 600 ms, to a seismic event of any of the intensity levels analyzed with a probability of 86.9%.
Security Evaluation of Open Access Wi-Fi Networks in a Public University in Junin, Peru

This article develops a topic of interest for users who can access the Internet and do not know the dangers of connecting to a free access network where the administrator of this is unknown, so a connection to this network would not be recommended. The central theme of this research is to demonstrate the risks of connecting to unknown access networks. The methodology used was the evaluation of how many users were being connected to the network created to monitor. The data obtained from users already connected have also been evaluated. According to the days they were connected through the networks, there is a strong demand for connected users. The conclusions show that some users are unaware of the dangers of connecting to one of these networks mentioned above and the risk of the amount of information that a person connected to the same network can obtain with the help of programs already created for its correct use. This work is a product of evaluating the dangers of connecting to an unknown network in Huancayo, Peru.
Cosine modulated filter bank‐based architecture for extracting and fusing saliency features

Abstract Many academics are interested in content‐based image retrieval techniques like image segmentation. In computer vision, the most popular method for segmenting a digital image into different parts is known as image segmentation. We assigned the artificially intelligent algorithm to the image's critical areas by modeling human features in specific regions. In order to detect the object and identify the key parts in the ‘RGB’ photographs, we combined scenes based on a colour and depth map, or ‘RGB‐D’, and used cosine modulated filter bank (CMFB), which conducts cross‐scale extraction of joint features from the images during feature extraction. The proposed ‘CMFB’ combines the discovered collaborative elements with the discovered supplementary data. The features in multi‐scale images is combined using fusion blocks with the goal of producing additional features (FB). Then, a saliency mapping calculation is made for the loss linked to two blocks. The suggested ‘CMFB’ is tested with the aid of five data sets, and it is shown that, the proposed ‘CMFB’ outperforms other conventional techniques.
Object counting in remote sensing via selective spatial‐frequency pyramid network

Abstract The integration of remote sensing object counting in the Mobile Edge Computing (MEC) environment is of crucial significance and practical value. However, the presence of significant background interference in remote sensing images poses a challenge to accurate object counting, as the results are easily affected by background noise. Additionally, scale variation within remote sensing images presents a further difficulty, as traditional counting methods face challenges in adapting to objects of different scales. To address these challenges, we propose a selective spatial‐frequency pyramid network (SSFPNet). Specifically, the SSFPNet consists of two core modules, namely the pyramid attention (PA) module and the hybrid feature pyramid (HFP) module. The PA module accurately extracts target regions and eliminates background interference by operating on four parallel branches. This enables more precise object counting. The HFP module is introduced to fuse spatial and frequency domain information, leveraging scale information from different domains for object counting, so as to improve the accuracy and robustness of counting. Experimental results on RSOC, CARPK, and PUCPR+ benchmark datasets demonstrate that the SSFPNet achieves state‐of‐the‐art performance in terms of accuracy and robustness.
Span-Prototype Graph Based on Graph Attention Network for Nested Named Entity Recognition

Named entity recognition, a fundamental task in natural language processing, faces challenges related to the sequence labeling framework widely used when dealing with nested entities. The span-based method transforms nested named entity recognition into span classification tasks, which makes it an efficient way to deal with overlapping entities. However, too much overlap among spans may confuse the model, leading to inaccurate classification performance. Moreover, the entity mentioned in the training dataset contains rich information about entities, which are not fully utilized. So, in this paper, a span-prototype graph is constructed to improve span representation and increase its distinction. In detail, we utilize the entity mentions in the training dataset to create a prototype for each entity category and add prototype loss to adapt the span to its similar prototype. Then, we feed prototypes and span into a graph attention network (GAT), enabling span to automatically learn from different prototypes, which integrate the information about entities into the span representation. Experiments on three common nested named entity recognition datasets, including ACE2004, ACE2005, and GENIA, show that the proposed method achieves 87.28%, 85.97%, and 79.74% F1 scores on ACE2004, ACE2005, and GENIA, respectively, performing better than baselines.
An automated face mask detection system using transfer learning based neural network to preventing viral infection

Abstract As the “Internet of Medical Things (IoMT)” grows, healthcare systems can collect and process data. It is also challenging to study public health prevention requirements. Virus transmission can be prevented by wearing a mask. The World Health Organization (WHO) recommends wearing a facemask to protect against the COVID‐19 pandemic—the levels of a pandemic rise across almost all regions of the world. By following the WHO rules, we support the development of face mask‐detecting technologies and determine whether or not people are using masks in public locations. The proposed paradigm in this paper will work in three stages. Firstly, we use an Image data generator to import the images. In addition to using a Haar cascade (HC) classifier for detecting faces, residual learning (ResNet152V2) trains a model that detects whether someone is wearing a face mask. Detection and classification are carried out in real‐time with high precision. Compared with other recently proposed methods, the model achieved 99.65% accuracy during training and 99.63% during validation.
A Control Strategy for Mechatronic Action of a Pipe Organ Using a VCM Actuator

In pipe organs, the oldest and most commonly chosen system for connecting the organist operator to the source of sound (i.e., the pipes to the action) is mechanical action. This article presents a mechatronic action with a voice coil motor (VCM) actuator to reproduce the action of a mechanical action on pipe organ. The mechatronic action makes it feasible to mechanically separate the keyboard from the pipes and to determine the control strategy for the mechatronic action by utilizing sensors, an actuator and a microcontroller. The time response of the organ pipe with mechanical action and the requirements for mechatronic action were outlined. The control strategy was preceded by measurements of the mechanical action and measurements of the behavior of the VCM actuator system, which moves the pneumatic valve pallet. Two control strategies, open-loop and closed-loop, were proposed and analyzed for the mechatronic action with the VCM actuator. According to the results, the suggested control strategies successfully reproduce the mechanical action’s behavior to a good extent.
φunit: A Lightweight Module for Feature Fusion Based on Their Dimensions

With the popularity of mobile devices, lightweight deep learning models have important value in various application scenarios. However, how to effectively fuse the feature information from different dimensions while ensuring the model’s lightness and high accuracy is a problem that has not been fully solved. In this paper, we propose a novel feature fusion module, called φunit, which can fuse the features extracted by different dimensional networks according to the order of feature information with a small computational cost, avoiding the problems of information fragmentation caused by simple feature stacking in traditional information fusion. Based on φunit, this paper further builds an extremely lightweight model φNet, which can achieve performance close to the highest accuracy on several public datasets under the condition of very limited parameter scale. The core idea of φunit is to use deconvolution to reduce the discrepancy among the features to be fused, and to lower the possibility of feature information fragmentation after fusion by fusing the features from different dimensions sequentially. φNet is a lightweight network composed of multiple φunits and bottleneck modules, with a parameter scale of only 1.24 M, much smaller than traditional lightweight models. This paper conducts experiments on public datasets, and φNet achieves an accuracy of 71.64% on the food101 dataset, and an accuracy of 75.31% on the random 50-category food101 dataset, both higher than or close to the highest accuracy. This paper provides a new idea and method for feature fusion of lightweight models, and also provides an efficient model selection for deep learning applications on mobile devices.
Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning

Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs.
Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition

With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from −16 dB to −8 dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from −16 dB to −8 dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments.
Dynamic Split Body: Changing Body Perception and Self-Location by Manipulating Half-Body Position

Some studies have been conducted to induce the sense of being in two locations by inducing body ownership in two bodies. However, the feeling of being in two locations was weak. In our previous study, we induced body ownership in a split avatar in advance to induce the sensation of being at two locations. As a result, although participants’ self-location extended to the right, the split avatar was perceived as a single body and they did not feel as if they were in two locations. Our demonstration expand our previous work by allowing participants to dynamically manipulate the position of the split body. We attempt to present an experience in which the split body is perceived as one body even though it is split, or the participants feel as if they are in two locations as two independent bodies. We also changed tracking system from OptiTrack to Lighthouse.
Quest Driven Spatial Songs: Exploring how narrative structures of Quests in Games can be mapped onto songs.

This paper briefly examines the relationship between music recordings and narrative structures found in video games. It highlights the immersive and time-consuming nature of game quests, where players invest substantial time exploring virtual worlds to reveal the narrative. The paper explores the potential of spatialised music experiences, particularly in the context of Dolby Atmos and the concept of degrees of freedom (3DoF and 6DoF). It investigates the application of spatial algorithms and game engines' middleware to create immersive music experiences that dynamically adapt to the listener's spatial and temporal position. The research acknowledges limited instances of studies on compositional narrative design frameworks specifically tailored for completely spatialised music, particularly regarding the virtual spatialisation of individual instruments. It recognizes that the prevalence of non-diegetic music in games, which serves to represent the player's emotional state, has impeded the exploration of spatialised music elements. Consequently, the paper proposes the adoption of narrative design frameworks derived from video games as a promising approach for songwriters to develop Quest Driven Spatial Songs (QDSS).
Data-Driven Expressive 3D Facial Animation Synthesis for Digital Humans

This doctoral research focuses on generating expressive 3D facial animation for digital humans by studying and employing data-driven techniques. Face is the first point of interest during human interaction, and it is not any different for interacting with digital humans. Even minor inconsistencies in facial animation can disrupt user immersion. Traditional animation workflows prove realistic but time-consuming and labor-intensive that cannot meet the ever-increasing demand for 3D contents in recent years. Moreover, recent data-driven approaches focus on speech-driven lip synchrony, leaving out facial expressiveness that resides throughout the face. To address the emerging demand and reduce production efforts, we explore data-driven deep learning techniques for generating controllable, emotionally expressive facial animation. We evaluate the proposed models against state-of-the-art methods and ground-truth, quantitatively, qualitatively, and perceptually. We also emphasize the need for non-deterministic approaches in addition to deterministic methods in order to ensure natural randomness in the non-verbal cues of facial animation.
Beyond the tropicalization of concepts: theorizing digital realities with and from the Global South (introduction to a special issue)

Abstract In this introductory section of the special issue titled “Beyond the Tropicalization of Concepts: Theorizing Digital Realities With and From the Global South,” the editors present a critical examination of the unquestioned adoption of theoretical frameworks from dominant regions of power and knowledge in the context of the Global South. This process, referred to as the “tropicalization of concepts,” is scrutinized for its consequences, which include the unequal significance of concepts, the naturalization of certain realities, the one-sided flow of ideas, and their solidification in public policies. In response to these challenges, the eight articles included in this special issue contribute valuable insights into innovative approaches for studying, theorizing, and engaging with the unique digital realities of the Global South. These articles shed light on diverse aspects of digital media use in different regions and offer fresh perspectives on how to conceptualize and address the associated issues.
Predictions in the Artificial Era

Abstract The final chapter describes the human motivation for prediction, presents opinions and predictions about the future, and possible strategies to cope with it. First, it presents a study on Artificial Intelligence impact summarizing the opinions of hundreds of participants in tech communities. Next, it contrasts Artificial General Intelligence and Narrow Artificial Intelligence. Then, it reviews the literature on consciousness from the fields of neurology, cognitive psychology, behavioral neuroscience, theoretical computer science, and philosophy. It complements the literature review with ideas formulated in Greek mythology and science fiction, as well as experiences and concepts of consciousness narrated by a yogi. The chapter returns to a more practical side describing privacy issues and points out that we are in a critical phase of a Technological Progress wave. The chapter talks about inequality and describes some recent ideas to reduce it. Besides, it presents an extensive study on ethics and machine decision-making. Finally, the chapter reviews elements of harmonious and brutal societies and discusses concerns on smart technologies for defense and attack. Finally, it briefly summarizes possible strategies to harness technology for development in the coming years.