Reviewerly/exHarmony huggingface dataset:
Digital Literacy and Autonomous Learning at a Mexican Escuela Normal

Future teachers must recognise the importance of digital literacy and autonomous learning in their learning and future pedagogical practice. This chapter explores students’ perceptions of English as a foreign language of digital literacy and autonomous learning in an Escuela Normal (EN) in central Mexico, an educational institution dedicated exclusively to teacher training. The results show that the participants consider that they have sufficient digital literacy skills for learning English online. Regarding autonomous learning, the data revealed that students prefer to learn with an approach that gives them more autonomy in learning English instead of more traditional approaches.
The Smart Contracts in the Oil, Gas and Petrochemical Industry

Purpose: A smart contract represents an agreement between parties that exploits a blockchain as the enforcing medium, limiting or avoiding the need of a legal enforcement (e.g., a court). The purpose of this paper is the review advantage and challenges of smart contracts on Oil, Gas &amp; Petrochemical Industry. Methodology: This paper does a systematic review to discuss the application prospects of blockchain technology in the oil, gas &amp; Petrochemical industry. Findings: The results show that the smart contract would be effective in Oil, Gas &amp; Petrochemical industry. Originality/Value: Finally, we note that, despite its significant potential, smart contracting is still a developing technology and has several open challenges associated with its implementation, such as privacy concerns, risk of cyber-attacks (such as hacking) and the energy required for computation and blockchain deployment of the contracts. So far, smart contract applications in energy systems have been mostly focused on research, proof-of-concept and demonstration projects (such as P2P demonstration projects run in a local community or microgrid).
Penguatan Hasil Belajar Peserta Didik Terhadap Assesmen Dengan Memanfaatkan Paradigma Baru

In the 21st century learning period requires educators to be more skilled in processing learning media as creatively as possible by utilizing current technological developments. Utilization of this technology can be used as a solution to improve student learning outcomes in understanding HOTS assessment. This research was conducted at Kedungdalem 2 Public Elementary School Class 5 by focusing on the material "Human Circulatory System" which was considered difficult for students to understand. In its implementation, the researchers utilized a 4D application-based learning media, namely "DEVAR" as a learning medium with PjBL and Scientific learning models. The method in research is Classroom Action Research which is a controlled investigative process to find and solve learning problems in the classroom. The results obtained in conducting classroom action research using the "DEVAR" application were carried out well by the researchers, so that the initial pre-test increased by 26.93% and the post-test increased to 42.30%.
Studies on Digital Storytelling Literature Education by Using Metaverse Tools

This paper deals with digital literature pedagogues suitable for humanities major students in the digital age. It introduces digital storytelling projects using metaverse tools in order to enhance humanities major students' understanding of the metaverse world and the rapidly changing digital technologies. To do this, the paper focuses on the class room environment that mainly discusses the differences between virtual reality and metaverse world, the origin of the word, metaverse, new digital literary works like VR literature and adaptation VR works such as Virtually Ulysses, Hamlet 360, etc. Lastly, this paper presents the way in which a project based learning for college literature classes can be done by using metaverse tools such as CoSpaces, Google Earth, and Zepeto Studio, where students create their own digital storytelling without complicated coding skills. (Chungnam University)
Webpage Duplication using Machine Learning

Web search engines face challenges with nearly identical and duplicate webpages, leading to increased storage requirements and slower results. Researchers have addressed this issue by exploring methods to detect such similarities. One approach involves utilizing sentence-level characteristics alongside fingerprinting techniques. The proposed method employs K-mode clustering for large sets of web documents and compares fingerprints and sentence features to identify almost identical web pages quickly and accurately. Experimental results on web page collections validate the effectiveness of this approach.
Implementation of Blockchains as a Distributed Ledger

The need for security and privacy calls into question the need for a model which control a large quantity of personal information. Block chain is used to link blocks using cryptography. SHA256 algorithm is used to implement blockchains. It consists of a database that is placed across the nodes on a peer-to-peer network where a copy of the ledger and updates are made independently. Once the update is made it cannot be altered without altering the subsequent blocks. The block chains concept can be extended to form a good solution for problems related to trust issues in society. Key Words: blockchain, distributed ledger, SHA256, security
Pemanfaatan Tranformasi Digital Untuk Mendukung Proses Pembelajaran Siswa-Siswi SMA-IT Atta’awun Sukaresmi

In the modern era, digital transformation has changed nearly every aspect of our lives, and education is no exception. The advancements in information and communication technology have had a significant impact on how we learn, teach, and interact within the context of education. The utilization of digital transformation in the world of education is becoming increasingly important as it holds the potential to enhance the quality of learning and create a more inclusive, interactive, and adaptive learning environment, as seen at Attaawun Sukaresmi High School. The aim of this research is to understand how students effectively utilize digital media. This study will employ a qualitative approach with a case study method. Data will be gathered through interviews with various stakeholders at Attaawun Sukaresmi High School, including teachers and students. Additionally, an analysis of the utilization of digital media at the school will be conducted. The results of this research are expected to provide a deeper understanding of the use of digital media such as smartphones, laptops, and others. Furthermore, concrete strategies and steps will be proposed that can be taken by the government, institutions, or schools to optimize the positive use of digital media. Thus, Attaawun High School can serve as an example for other schools in coping with the changing times due to the development of digital technology.
Violence detection in railway environment with modern deep learning approaches and small dataset

Passengers’ security is a continual challenge for public transport operators. For a few years now, cameras are installed inside vehi- cles (train, metro, tramway, bus. . .) but encountered numerous issues. The aim of this study is to propose new video based violence detection algorithms applied to a railway environment exploiting modern deep learning architectures. We have done a domain adaptation of a state-of-the-art architecture selected from the research community on a recorded railway dataset. Then to improve the selected architecture we have proposed to consider a long-term sequence with a recurrent mechanism. Trained architectures have given promising results on selected datasets of the community. The results obtained on our new railway dataset show that 3D convolutional network combined with recurrent networks can model and detect violence in complex railway environment.
A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as A Proxy

<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iquaflow</small> is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iquaflow</small> measures quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem involves finding images that, while being compressed to their smallest possible file size, still maintain sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iquaflow</small> is suitable for such case. All this development is wrapped in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mlflow</small> : an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookiecutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub.
Discovering Adaptable Symbolic Algorithms from Scratch

Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot adaptable policies from scratch. In contrast to neural network adaption policies, where only model parameters are optimized, ARZ can build control algorithms with the full expressive power of a linear register machine. We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes. We demonstrate our method on a realistic simulated quadruped robot, for which we evolve safe control policies that avoid falling when individual limbs suddenly break. This is a challenging task in which two popular neural network baselines fail. Finally, we conduct a detailed analysis of our method on a novel and challenging non-stationary control task dubbed Cataclysmic Cartpole. Results confirm our findings that ARZ is significantly more robust to sudden environmental changes and can build simple, interpretable control policies.
BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking

Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in realtime is highly desirable. In this paper, we present BodySLAM++, a fast, efficient, and accurate human and camera state estimation framework relying on visual-inertial data. BodySLAM++ extends an existing visual-inertial state estimation framework, OKVIS2, to solve the dual task of estimating camera and human states simultaneously. Our system improves the accuracy of both human and camera state estimation with respect to baseline methods by 26 % and 12%, respectively, and achieves realtime performance at 15+ frames per second on an Intel i7-model CPU. Experiments were conducted on a custom dataset containing both ground truth human and camera poses collected with an indoor motion tracking system.
Pengembangan Web Interaktif "Hai Si IPA" Untuk Meningkatkan Literasi Sains Di Sekolah Dasar

The key to students facing challenges in the 21st century is insight and skills in science literacy. In Indonesian elementary schools, it is noted that students' science literacy is still not maximally utilizing digital technology to learn science and science learning outcomes are still low. Based on the results of observations and interviews in elementary schools, it was found that blog teaching materials had never been used on ecosystem material, so the researcher's goal was to develop a blog teaching material "Hi Si IPA to improve the science literacy of grade V elementary school students. This research uses the ADDIE model (Analysis- Design- Development -Implement -Evaluate). Quantitative data analysis in the form of numbers from the results of expert validation of teaching materials, questionnaires of attractiveness, practicality, effectiveness, namely pretests and posttests. While qualitative data analysis is in the form of criticism and suggestions by teaching material experts, teachers, and students. The results of teaching material expert validation are 81% with very valid criteria. The trial of the "Hi Si IPA" blog in a small group of 5 students with a questionnaire of students' initial knowledge and a pretest questionnaire the results were low, while the results of the student interest questionnaire were 89% with very interesting criteria. The field trial was 25 students in class V, the results of the student and teacher practicality questionnaire were 88.5%. Analysis of the increase in science literacy using the normality gain test from the pretest and posttest results of students was found to increase 84% with high criteria. So that the blog "Hi Si IPA" is feasible to use, attracts students' interest, is practical for learning, and can improve science literacy.
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models

Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we propose learning diffusion models as priors. We then can sample directly from the posterior trajectory distribution conditioned on task goals, by leveraging the inverse denoising process of diffusion models. Furthermore, diffusion has been recently shown to effectively encode data multi-modality in high-dimensional settings, which is particularly well-suited for large trajectory dataset. To demonstrate our method efficacy, we compare our proposed method - Motion Planning Diffusion - against several baselines in simulated planar robot and 7-dof robot arm manipulator environments. To assess the generalization capabilities of our method, we test it in environments with previously unseen obstacles. Our experiments show that diffusion models are strong priors to encode high-dimensional trajectory distributions of robot motions. https://sites.google.com/view/mp-diffusion
GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation

Language-Guided Robotic Manipulation (LGRM) is a challenging task as it requires a robot to understand human instructions to manipulate everyday objects. Recent approaches in LGRM rely on pre-trained Visual Grounding (VG) models to detect objects without adapting to manipulation environments. This results in a performance drop due to a substantial domain gap between the pre-training and real-world data. A straight-forward solution is to collect additional training data, but the cost of human-annotation is extortionate. In this paper, we propose Grounding Vision to Ceaselessly Created Instructions (GVCCI), a lifelong learning framework for LGRM, which continuously learns VG without human supervision. GVCCI iteratively generates synthetic instruction via object detection and trains the VG model with the generated data. We validate our framework in offline and online settings across diverse environments on different VG models. Experimental results show that accumulating synthetic data from GVCCI leads to a steady improvement in VG by up to 56.7% and improves resultant LGRM by up to 29.4%. Furthermore, the qualitative analysis shows that the unadapted VG model often fails to find correct objects due to a strong bias learned from the pre-training data. Finally, we introduce a novel VG dataset for LGRM, consisting of nearly 252k triplets of image-object-instruction from diverse manipulation environments.
Status of a breast CT phantom dataset from breast MR images for PHENOMENO project

This work rises from a framework of the Phenomeno European project and aims at developing a dataset of digital breast models to be used in in-silico tests and anthropomorphic phantom manufacturing for testing and developing apparatuses in x-ray breast imaging. A software was developed for classifying voxels in MRI breast images in air, glandular tissue, adipose tissue, skin or tumoral lesion. Such a software was based on pixel values and determined thresholds. The developed algorithm was tested on MRI breast images taken from an open access database. In order to validate the segmentation process, two approaches were introduced. A first approach is based on a subjective evaluation of an experienced radiologist, meant to evaluate to which extent the glandular tissue distribution and 3D conformation was preserved during the tissue classification as well as to evaluate to which extent fine details contained in the clinical images were preserved in the classified ones. Hence, two evaluation grids were proposed with scores ranging between 1 (lower grade) and 5 (higher grade). Subsequently, an objective analysis was introduced based on the use of dice similarity index (DSI) between images classified by the developed algorithm and images manually segmented by an experienced radiologist. In the two investigated cases, DSI resulted 0.934 and 0.755; when DSI was evaluated for segmented images provided within the database containing the breast MRI images, these values reduced to 0.835 and 0.488, for the same cases. The subjective evaluation is still ongoing.
Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring

Military, construction, picture and video mapping, medical, search and rescue, parcel delivery, covert area survey, oil rig and power line monitoring are just a few of the applications for unmanned aerial vehicles (UAVs). UAVs are gaining popularity as a model for agricultural, wireless communications and aerial surveillance, as well as service and delivery. Unmanned aerial vehicle (UAV) research has attracted a lot of interest in recent decades due to the rapid growth of computer technology, autonomous control technology, and communication technology. In various industries, including aviation, unmanned systems are replacing manned systems. Due to their high success rates in military and civilian missions, unmanned aerial vehicles (UAVs), one of the most popular and successful unmanned systems, are gradually becoming an important part of the industry. air. The biggest problem with drones is finding the best trajectory in difficult conditions. It seeks to tour all control locations as efficiently as possible in order to receive information from sensors set in wide areas. In this paper, we employed a Genetic Algorithm (GA) to discover the best flight path for unmanned aerial vehicles (UAVs) in a three-dimensional environment, i.e. space. One of the important elements in the suggested strategy is solving the travelling salesman problem (TSP) to find the optimal path. In the future, we can enhance the complexity of our problem through the addition of new constraints imposed by the dynamic environment in which we operate. The experimental results suggest that GA can be used to optimize UAV path planning.
An Interactive System for Multiple-Task Linear Temporal Logic Path Planning

Beyond programming robots to accomplish a single high-level task at a time, people also hope robots follow instructions and complete a series of tasks while meeting their requirements. This paper presents an interactive software system that consists of a multiple-task linear temporal logic (LTL) path planner and a human-machine interface (HMI). The HMI transforms human oral instructions into task commands that can be understood by the machine. The planner grows a rapid random exploring tree to search for solutions for multiple tasks. When switching tasks, the search tree is re-initialized and reconnected to utilize the information gathered during the exploration of the workspace. The feasibility of the improved planner is theoretically guaranteed, and profiling in simulation shows an acceleration in planning. An experiment with a quadcopter is conducted to show that the combination of the multiple-task LTL planner and the HMI results in a synergistic effect in real-world applications.
gatekeeper: Online Safety Verification and Control for Nonlinear Systems in Dynamic Environments

This paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method to ensure that nonlinear systems can operate safely in dynamic environments despite limited perception. gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step that ensures that proposed trajectories can be executed safely, despite nonlinear dynamics subject to bounded disturbances, input constraints and partial knowledge of the environment. Our key contribution is that (A) we propose an algorithm to recursively construct committed trajectories, and (B) we prove that tracking the committed trajectory ensures the system is safe for all time into the future. The method is demonstrated on a complicated firefighting mission in a dynamic environment, and compares against the state-of-the-art techniques for similar problems.
On Sensitivity of Data Models w.r.t. Training Data

In this article, we suggest a way how to quantify the sensitivity of a data model w.r.t. training data at a certain input by using a derived model having a single data point as artificial parameter, and we relate our definition of sensitivity w.r.t. training data to the complexity of a data model. In case of linear regression and ridge regression we give an explicit expression for the so defined sensitivity w.r.t. training data and study the properties. Moreover, we discuss the numerical approximation of sensitivity w.r.t. training data for neural networks and provide an application of this numerical approximation to remote sensing in the environmental sciences.