Reviewerly/exHarmony huggingface dataset:
Support Vector Machines: Spherical Separability: Limitations of Learning

In this research paper, support vector machines based on the concept of spherical separability are proposed. It is reasoned that due to the mismatch between training data based decision boundary and model decision boundary adver-sarial patterns can be synthesized leading to misclassification. Also limitations of learning paradigms are highlighted. Also, some approaches to mitigate effects of adversarial attacks are proposed.
Constructing Extreme Learning Machines with zero Spectral Bias

The phenomena of Spectral Bias, where the higher frequency components of a function being learnt in a feedforward Artificial Neural Network (ANN) are seen to converge more slowly than the lower frequencies, is observed ubiquitously across ANNs. This has created technology challenges in fields where resolution of higher frequencies is crucial, like in Physics Informed Neural Networks (PINNs). Extreme Learning Machines (ELMs) that obviate an iterative solution process which provides the theoretical basis of Spectral Bias (SB), should in principle be free of the same. This work verifies the reliability of this assumption, and shows that it is incorrect. However, the structure of ELMs makes them naturally amenable to implementation of variants of Fourier Feature Embeddings, which have been shown to mitigate SB in ANNs. This approach is implemented and verified to completely eliminate SB, thus bringing into feasibility the application of ELMs for practical problems like PINNs where resolution of higher frequencies is essential.
Comparative analysis in real environment of trajectory controllers on ROS2

This article presents a comparative analysis of three trajectory controllers - DWB, RPP, and MPPIC - implemented in Navigation2, a widely used navigation package in the ROS2 robotics platform. The aim is to assess the performance of these controllers in terms of trajectory time, distance traveled, motor effort, goal error, and computational resource consumption. Real-world scenarios were considered to evaluate their effectiveness and efficiency. The findings, highlighted in the article, yielded initial conclusions. In the first environment without obstacles, the DWB controller displayed superior performance, achieving the shortest time of 6.87 seconds, low effort, and the shortest distance covered. However, none of the controllers attained an exact desired pose. In the second environment, all controllers achieved a 100% success rate, with the MPPIC controller being the fastest at 9.66 seconds, requiring less effort and covering a shorter distance than the RPP controller. Overall, the MPPIC controller emerged as the most efficient, utilizing fewer computational resources while maintaining a high success rate.
From Game AI to Metaverse AI: Realistic Simulation Training as a New Development Direction for Game Artificial Intelligence

As games have evolved, the metaverse has emerged as the next stage of gaming. As an essential module of the metaverse, metaverse AI has become an increasingly popular and researched topic. The development process from game AI to metaverse AI raises questions about the development direction of AI. To investigate the relationship between the new stage of game development and the further evolution of artificial intelligence, and to explain the role of the metaverse in the development of AI, this paper analyzes the development stages and characteristics of games, summarizes the development process and structure of the metaverse, and proposes a unique game classification method to describe the mapping relationship between games and the metaverse, and to explain the similarities and differences between games and the metaverse. The paper systematically describes the current development status of AI and provides examples of the technological development and application fields of AI from game AI to metaverse AI based on the relationship between games and the metaverse. Finally, the paper analyzes and summarizes the differences and inherent reasons between the metaverse and games in the field of AI, providing a new perspective and exploration method for the relationship between games and the metaverse and suggesting the importance of the metaverse for the development of AI research. This study not only helps people to understand the characteristics and development trends of the metaverse but also promotes the development of game AI technology and provides strong support for the construction of the metaverse.
Self-Supervised Temporal Consistency applied to Domain Adaptation in Semantic Segmentation of Urban Scenes

Deep Learning-based Semantic Segmentation has reached remarkable levels of accuracy throughout the years. Nonetheless, applying pretrained models to new domains—even though related—leads to considerable drops in performance due to domain shift. In addition to that, semantic labels are costly to obtain for new application scenarios, what makes the process of transfer learning even harder. Finally, since the majority of works in semantic segmentation rely on single-frame predictions, they miss a huge opportunity offered by reasoning on the temporal properties of video data. In light of that, this work addresses all previous problems by implementing a domain adaptation strategy based on self-supervised learning of temporal consistency in videos. The results show that our strategy greatly improves model precision and stability on a given unlabeled target domain.
Blockchain Technology Provides Machine Learning, Cloud Computing and Secure Data Transmission

Blockchain technology is revolutionizing industries all over the world. Due to recent developments in machine learning, new technologies like cloud computing and safe data exchange have evolved. The collecting and processing of training data on centralized systems is required for conventional machine learning methods. New decentralized machine learning algorithms and cloud computing have made ML on-device information learning possible. IoT devices may use cloud computing services to outsource training duties, enabling AI at the network edge. Nevertheless, these scattered edges intelligence systems introduce new problems, such as concerns over user privacy and information security. Blockchain has been suggested as a workable solution to these problems. Due to its decentralized, accessible, and secure structure, blockchain has developed into a breakthrough invention for the future of numerous sectors' purposes. Additionally, this system has reliable automated scripting execution and immutable information recorders. Recently, as quantum technologies have become more practical, blockchain has begun to face potential threats from quantum computing. In this work, we summaries the literature in the research domains of blockchain-based cloud computing, machine learning, and safe data sharing in order to offer a synopsis of the present state-of-the-art in these cutting-edge developments. We also provide a basic introduction to post-quantum blockchain.
Deep Reinforcement Learning for Mapless Robot Navigation Systems

DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.
Using Generalized (L, G)-Codes for Implementation the Classic McEliece Cryptosystem

In this paper, we consider options for modifying algorithms for constructing a check matrix and finding error positions for a binary generalized ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L, G$</tex> ) -code with a separable Goppa polynomial, and calculate the required number of operations for implementing these algorithms.
Suspicious Activity Detection Based on Head Movement

Detecting suspicious activity based on a person's head movement, makes the task of monitoring more efficient and automated. In this project, we aim to detect such suspicious movements based on head movement direction being left or right associated with a counter variable using the media pipe module in python, and then the machine can itself trigger certain notification capabilities to registered mobile numbers using Twilio API on the occurrence of such tasks, making it scalable and reliable for applications like exam monitoring, ATM thefts, etc.
Detection of Asymmetric Anomalies in Mammograms using Dynamic Time Warping for Early Breast Cancer Identification

Breast cancer is a leading cause of mortality, necessitating effective early detection techniques. This study investigates the use of Dynamic Time Warping (DTW) for assessing breast asymmetry in mammographic images to improve early cancer diagnosis. A clinical dataset of 115 patients with BIRADS classifications ranging from B1 to B5 was utilized. DTW was applied to measure differences between bilateral mammograms and quantify asymmetry. Significant variations in distances were observed across BI-RADS groups, with mean distances ranging from 1.78e+03 to 2.39e+03. This distinction indicates the effectiveness of DTW in capturing and quantifying asymmetrical patterns, particularly in cases categorized as BIRADS 1 and 2. Moreover, 36 cases with possible asymmetry were identified, demonstrating notable differences between the corresponding regions of contralateral breasts, predominantly in BI-RADS 3 to 5. Statistical analysis provided insights into the distribution and characteristics of asymmetry within each BIRADS group, underscoring the diagnostic value of BI-RADS in assessing breast asymmetry. Additionally, nipple retraction as an additional indicator enhanced the diagnostic evaluation, serving as an early indicator of breast cancer and strengthening the correlation between asymmetry and malignancy. In conclusion, DTW implementation offers a promising approach for early breast cancer detection through assessing asymmetry. Combining DTW distance analysis with additional indicators can enhance the diagnostic process, ultimately improving patient outcomes.
Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles

This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Soft Actor-Critic method, producing a distributed and distributional variant named PDSAC, and compared it with a second one based on the traditional SAC algorithm. In addition, we also embodied a prioritized memory system into them. The UAV used in the study is based on the Hydrone vehicle, a hybrid quadrotor operating solely in the air. The inputs for the system are 23 range findings from a Lidar sensor and the distance and angles towards a desired goal, while the outputs consist of the linear, angular, and, altitude velocities. The methods were trained in environments of varying complexity, from obstacle-free environments to environments with multiple obstacles in three dimensions. The results obtained, demonstrate a concise improvement in the navigation capabilities by the proposed approach when compared to the agent based on the SAC for the same amount of training steps. In summary, this work presented a study on deep reinforcement learning applied to mapless navigation of drones in three dimensions, with promising results and potential applications in various contexts related to robotics and autonomous air navigation with distributed and distributional variants.
Autonomous Navigation of Wheelchairs in Indoor Environments using Deep Reinforcement Learning and Computer Vision

We present a novel method for autonomous navigation of motorized wheelchairs in shared indoor environments with humans, utilizing Deep Reinforcement Learning and Computer Vision. The primary goal of this research is to enhance the quality of life for individuals with disabilities who depend on such assistance for mobility. Our approach combines the Deep Deterministic Policy Gradient (DDPG) algorithm with computer vision techniques, enabling motorized wheelchairs to autonomously navigate environments containing both static and dynamic obstacles. We conducted experiments within a simulated environment, and the results obtained showcase the promising potential of our proposed solution, thus contributing to the advancement of research in this field.
Container Scheduling in Kubernetes Clusters Using a Mixed Integer Linear Programming Approach

A large part of the services provided on the Internet resides in virtualized environments of “cloud” providers, whose IT infrastructures adopt, for the most part, the containers virtualization technique to house the various applications that must run independently and isolated. The management of such containers predominantly adopts the Kubernetes solution, whose standard algorithm for allocating containers on physical machines (nodes) is not capable of optimizing the cost/performance ratio of the cloud environment. As a solution to this problem, this work proposes a mixed integer linear programming approach based on the Capacitated Facility Location Problem to optimize the distribution of containers in nodes of a Kubernetes cluster, taking into account the cost of installing a new node, the history of failures of the nodes in operation and the consumption of resources by each application running in a container. The experimental results show that the proposed approach significantly improves the performance of Kubernetes cluster concerning the conventional scheduling strategy while ensuring the efficient execution of applications demands.
Ball Detection and Tracking with Different Embedded Systems in the RoboCup Soccer context

This paper presents a vision system for ball detection and tracking in the context of the RoboCup Soccer Humanoid League Kidsize category. The system aims to enable humanoid robots to autonomously play soccer by visualizing and interacting with the environment in real time. Previous approaches based on color and shape extraction have limitations due to recent rule changes mandating a partially white ball. To overcome these challenges, we propose the use of convolutional neural networks, specifically the You Only Look Once (YOLO) model, known for its efficiency and real-time performance in object detection. Optical flow, a technique analyzing pixel displacement between frames, is integrated to capture ball movement. Experimental evaluations compare different hardware platforms in terms of execution velocity and computational cost. Our results demonstrate the effectiveness of the developed vision system for ball detection and tracking in the Kidsize category. The YOLO model trained on the TORSO-21 dataset allows for fine-tuning, offering adaptability to changes in context without the need for a complete model overhaul. By incorporating optical flow within the ball area, accurate movement tracking is achieved. The analysis of different hardware platforms provides insights into execution speed, accuracy, computational cost, and the limitations imposed by size constraints. This research contributes to the advancement of computer vision applications in robotics and provides a foundation for further exploration and optimization in the field.
Identifying New Persons in the Context of the Robocup@Home Competition Using KNN

Robocup@Home proposes a challenge related to Person Recognition: after presented, a new ‘operator’ should become ‘immediately’ recognizable by the robot. The presentation procedure may require the operator to correctly interact with the robot, following a certain procedure, as instructed by the robot itself (for example, staying in front of the robot, so that the robot can take pictures of this person). In this paper, we propose the use of the KNN (K-Nearest Neighbor) supervised machine learning algorithm to include a new ‘operator’ in a database of persons recognizable by the robot. This algorithm uses information taken from an image segmentation of the face of the operator. The experiment evaluates how long it takes to include a new operator if the robot has from 1 to 12 current operators, evaluating also how long it takes to include this operator based on 1, 2 or more images of the new operator, taken from slightly different points of view. The results confirm that KNN can be used to ‘present to the robot’ up to 13 new operators, with up to 15 images for each operator, in less than 60 seconds.
A Study on Quantum Key Distribution Satellite Communications

Quantum key distribution (QKD) is a secure communication technique which uses quantum mechanics to protect communications. To overcome large distances, it requires the use of quantum repeaters, which are still challenging nevertheless feasible, or Twin-Field-QKD (TF-QKD) technology, which has been demonstrated several years ago. As it develops and matures, quantum technology is expected to play an increasingly major role in networks. Satellite QKD enables secure communication between devices via both satellites and ground stations. The study explores the transmission of quantum encryption technology in space and presents an overview of cubesats and satellites that currently use quantum key distribution (QKD) technology.
Spec-crawl: Domain specific crawler and specifications management for search engines

The web and characteristics of search engines have evolved in timely patterns adding technology and domain-specific context dimensions. It is challenging for traditional crawlers to retrieve information that the user needs explicitly, and a search engine has the task of quickly processing vast amounts of data. A domain-specific crawler can build a knowledge base of relevant data, and storing item data as associative can provide a scope to increase the processing computation efficiency. This research focuses on designing and developing a domain-specific crawler whose results are domain-related pages for user domains of interest from Wikipedia, generating clusters of specifications for users, and recommending the product based on user preferences. The former uses similarity measures and works through establishing parent-child relationships across URL. The latter uses correlation methods and paradigmatic similarity measures to classify the specifications using the design of essential clusters. The item specifications are experimented with thresholds to find the most suitable key-value pairs to establish the relevancy of recommendations. Similarity measures are used to bring out the relevance. The paper further presents the results and discussion of each module as applied to Wikipedia data and e-commerce data sets. The process promises to be effective and can contribute to state-of-art search engine designs.
Unlocking the Power of Artificial Intelligence and Machine Learning in Transforming Marketing as We Know It

Technological changes have helped the methodology of execution where we can examine the sector in addition to the manner enterprise is carried out. Productive development has facilitated correct manufacturing in large volumes. The advanced technology, together with information in analytics, smart devices, computerization, and artificial intelligence (AI), is converting the system of society and persons to interact on a basic level. The evolution and development of artificial intelligence (AI) have transformed the effectiveness of commercial enterprises globally. However, business adoption of AI-based total packages by businesses remains constrained because of a lack of know-how approximately the generation. The technology can assist the organisation in understanding the clients and permit it to target the customers better and personalize advertising and marketing movements and messages. Advertising takes the benefit of facts to big data: consumer needs research, marketplace analyses, client insights, and competition intelligence.
Transformation in the Classroom

This chapter is the result of the study of the way in which microcontents were incorporated in the hybrid modality in the first semester (January – June 2023) in the different learning units taught in the areas of institutional, humanistic, scientific, and basic technological training at the upper secondary level of the Instituto Politécnico Nacional. The research has a quantitative approach with a descriptive scope based on a comparative approach. The sample population was made up of 150 students from the second, fourth, and sixth semesters to whom the questionnaire is applied as the data collection instrument. The results indicate that teachers incorporate as main materials videos, texts, diagrams, and drawings; on the part of the students, they state that the incorporation of these favored motivation, concentration, access, and creation of content. The chapter addresses pedagogical implications and makes recommendations for the integration of microcontent in the hybrid modality.
Comparison of the sensitivities and accuracies of optoelectronic transducers for solar irradiance measurement

Abstract This paper compares the sensitivities and accuracies of four different optoelectronic transducers in measuring solar irradiance for the fabrication of portable and low-cost pyranometers. Trans-impedance conditioning circuits were designed for two phototransistors (BP103 and SFH3310) and two photodiodes (BPW21 and BPW34). The Arduino Mega 2560 was used as an interface between the analogue signal produced by the conditioning circuits and the digital output ports. The transducers with a standard pyranometer RSRA_05V were arranged on a vero board and exposed to the sun. Statistical analysis of the experimental results produced the least root mean square error value of 6.58794 Wm −2 for phototransistor BP103 during the dry season followed by 13.35216 Wm −2 for phototransistor SFH3310 during the dry season. The correlation coefficients of BPW21, BPW34, BP103, and SFH3310 with the standard pyranometer are 0.9489, 0.9916, 0.9976, and 0.9905, respectively. The experimental results obtained from the phototransistors BP103 and SFH3310 strongly correlated with those of the standard pyranometer coupled with lower root mean square error and mean bias error values than those of the photodiodes. Thus, the phototransistors BP103 and SFH3310 are more accurate and effective for measuring solar irradiance. This study contributes to the development of low-cost and accurate solar radiation meters.
Professionally Ethical Ways to Harness an Art-Making Generative AI to Support Innovative Instructional Design Work

Instructional designers often pride themselves on using the most cutting-edge commercial authoring and other tools available to achieve their work. Their creations have to meet high technical standards in order to function in a digital environment, in learning management systems, content management systems, on social media, on digital content platforms, and others. In the present moment, generative AI tools enable the making of novel texts and digital visuals, among others. A major extant question is how best to harness generative art-making AIs in instructional design work. In this case, this work explores professionally ethical (and legal) ways to use a generative art-making AIs for ID work, as an innovative approach based on a review of the literature, a year of using several free web-facing art-making generative AIs (CrAIyon, Deep Dream Generator, and others) in open or public beta, and learning from applied instructional design work (over several decades).