Reviewerly/exHarmony huggingface dataset:
FEDERATED LEARNING FOR VISION-BASED OBSTACLE AVOIDANCE IN MOBILE ROBOTS

Federated learning (FL) is a machine learning approach that allows multiple devices or systems to train a model collaboratively, without exchanging their data. This is particularly useful for autonomous mobile robots, as it allows them to train models customized to their specific environment and tasks, while keeping the data they collect private. Research Objective to train a model to recognize and classify different types of objects, or to navigate around obstacles in its environment. Materials and me¬thods we used FL to train models for a variety of tasks, such as object recognition, obstacle avoidance, localization, and path planning by an autonomous mobile robot operating in a warehouse FL. We equipped the robot with sensors and a processor to collect data and perform machine learning tasks. The robot must communicate with a central server or cloud platform that coordinates the training process and collects model updates from different devices. We trained a neural network (CNN) and used a PID algorithm to generate a control signal that adjusts the position or other variable of the system based on the difference between the desired and actual values, using the relative, integrative and derivative terms to achieve the desired performance. Results through careful design and execution, there are several challenges to implementing FL in autonomous mobile robots, including the need to ensure data privacy and security, and the need to manage communications and the computational resources needed to train the model. Conclusion. We conclude that FL enables autonomous mobile robots to continuously improve their performance and adapt to changing environments and potentially improve the performance of vision-based obstacle avoidance strategies and enable them to learn and adapt more quickly and effectively, leading to more robust and autonomous systems.
Designing an Augmented Reality Learning Experience for Library Instruction as a Technology Novice

This instructional design case describes the author’s process for creating an augmented reality learning experience for library instruction at an academic library. With no budget, no team, and little experience with this type of technology, this design case describes the development of an instructional design utilizing emerging technologies. It also addresses the constraints of solo work, small or nonexistent budgets, little no experience using the technology, and interest but no support for learning and implementing an innovative solution to a design problem
To the 65th Anniversary of the Library of the Institute of Economics and Organization of Industrial Production of the Siberian Branch of the Russian Academy of Sciences

A brief history of the library of the Institute of Economics and Organization of Industrial Production of the Siberian Branch of the Russian Academy of Sciences, which celebrates its 65 th anniversary in 2023, is presented.
The Effects of Using Spin Wheel Media on Counting Ability of Tunagrahita Students

This study aims to determine the effect of spin wheel media on the counting ability of tunagrahita students in class IV at SLB Negeri 2 Yogyakarta. This study used the one-group pre-test-post-test research method. Data collection in this study used written tests, and data analysis methods used non-parametric statistical data analysis techniques with the Wilcoxon Signed Rank Test (Wilxocon marked rank test) with the help of SPSS version 20. The results of Z value = -2.236 with Asymp Sig were obtained from the analysis results obtained in this study. (2-tailed) 0.025, which is below 0.05. From these results, it can be interpreted that Ho is rejected and Ha is accepted at an initial significance of 5%. Therefore, the hypothesis in this study, "There is an effect of using rotating wheels on the numeracy skills of tunagrahita students in class IV at SLB Negeri 2 Yogyakarta" can be accepted as true. So, this study can be concluded that there is an effect of using spin wheel media on the counting ability of grade IV tunagrahita students at SLB Negeri 2 Yogyakarta.
WebNORM: a web application for calculating normative mineralogy

Normative mineralogy is a valuable tool for interpreting the mineralogical composition of rocks based on their bulk rock geochemical data. Despite the development of various computational tools for normative mineralogy calculations, the accessibility and continued usage of many existing tools is limited by the use of older languages, licensing constraints, and restricted access to the source code. In this paper, we introduce a Python-based implementation of the normative mineralogy algorithm, which is accessible through a user-friendly web application webNORM , or as part of the pyrolite Python package. The algorithm uses major, minor, and trace element oxides as input and returns up to 31 normative minerals. It offers two methods for estimating the Fe 2 O 3 :FeO ratio. The web application offers a convenient interface for users without programming experience, while the pyrolite integration allows more advanced users to leverage the Python ecosystem for end-to-end geochemical data analysis. Comparison between our implementation of the normative calculation and previous implementations shows that our version is accurate and reliable. webNORM offers the geoscience community an accessible solution for calculating normative mineralogy. The algorithm and web application are open-source, and we encourage contributions and adaptations by the community for specific needs and improvements.
Model for Land Use Optimisation for Flood Management in Lasolo Watershed, Indonesia

Changes in land use from vegetation to non-vegetation in the Lasoso watershed contribute to an increase in the run-off coefficient caused by inundation during the wet season. Therefore, engineering is required to reduce discharge from surfaces. This study seeks to optimize land use in order to manage flooding. This study employs the paimin (modified) method with three different scenarios: Business as usual (BAU), simultaneous, and discrete. Results indicated that the BAU scenario has throughfall and retention capacity values of 15.55 percent and 19.39 percent of the total watershed area, respectively. scenario-2 (simultaneous) decreased throughfall and increased storage capacity (15.35 and 24.65%, respectively); scenario-3 (discrete) also improved. Comparing throughfall values reveals that the discrete simulation (-0.0020) is greater than the simultaneous scenario (-0.008), whereas the retention capacity has the same value (0.0020). The discrete simulation is superior to the simultaneous simulation in terms of optimizing its use for flood management in the Lasolo catchment.
Fakenews is much more than fake content

The goal of this article is simple. In the context in which there is more and more talk about the concept of fakenews, there are also efforts to instrumentalize and operationalize it. Rightly so, from media to commerce, from social networks to propaganda, the phenomenon is at the heart of the communication processes we use today, but also at the heart of the problems we face as societies. Unfortunately, fakenews is a much more subtle concept than people think and the big question is whether this phenomenon can be approached legally, without violating the right to opinion and free expression. Our article deals precisely with these subtleties of the concept and puts them in parallel with the normative basis that we now have as a reference. What can and cannot be done in this area ultimately depends on our ability to correctly understand the manifestations of what we call fakenews.
Wave Parameter Selection

Wave is a provably EUF-CMA (existential unforgeability under adaptive chosen message attacks) digital signature scheme based on codes [15]. It is a hash-and-sign primitive and its security is built according to a GPV-like framework [19] under two assumptions related to coding theory: (i) the hardness of finding a word of prescribed Hamming weight and prescribed syndrome, and (ii) the pseudo-randomness of ternary generalized $$(U|U+V)$$ codes. Forgery attacks (i)—or message attacks—consist in solving the ternary decoding problem for large weight [7], while, to the best of our knowledge, key attacks (ii) will try to exhibit words that are characteristic of $$(U|U+V)$$ codes, which are called type- $$U$$ or type- $$V$$ codewords in the present paper. In the current state-of-the-art, the best known attacks both reduce to various flavours of Information Set Decoding (ISD) algorithms for different regime of parameters. In this paper we give estimates for the complexities of the best known ISD variants for those regimes. Maximizing the computational effort, thus the security, for both attacks lead to conflicting constraints on the parameters. We provide here a methodology to derive optimal trade-offs for selecting parameters for the Wave signature scheme achieving a given security. We apply this methodology to the current state-of-the-art and propose some effective parameters for Wave. For $$\lambda =128$$ bits of classical security, the signature is 737 bytes long, scaling linearly with the security, and the public key size is 3.6 Mbytes, scaling quadratically with the security.
Predictive Analysis to Prevent Fraud and Reduce Risk for Preserved Food Processing Industry

Abstract: This paper presents a blockchain-based traceability system for storing and retrieving product information in the agricultural supply chain, aiming to address issues such as centralized management, lack of transparency, untrustworthy data, and fragmented information. By leveraging blockchain technology's decentralized, tamper-proof, and traceable nature, the system enhances the transparency and credibility of traceability information. To alleviate the burden on the blockchain and enable efficient information retrieval, a dual storage structure comprising both an on-chain and off-chain component is employed to store traceability data. Moreover, the combination of blockchain technology and cryptography ensures the secure sharing of private information within the blockchain network. A reputation-based smart contract is devised to incentivize network nodes to contribute traceability data. The system's performance analysis and practical application demonstrate its ability to enhance query efficiency, safeguard private information, ensure data authenticity and reliability in supply chain management, and meet real-world application requirements.
The application of English-Bahasa Indonesia compatibility in descriptive text to improve writing skill of grade VII 19 Junior High School, Bandung, academic year 2020-2021

Classroom Action Research in “The application of English-bahasa Indonesia compatibility in descriptive text to improve writing skill of Grade VII 19 Junior High School Bandung, Indonesia, in academic year 2020-2021” is purposed to intensify students’s competencies both in writing bahasa Indonesia and English. Classroom Action Reseach at this moment was undertaken in Bandung, from November 2016 until April 2017. It took six months to accomplish the purpose of this study. The mayor participants were Grade VII. with 250 students had participated in this study. This classroom action research activities had to be varied with daily activities in learning English, this cycle had been implemented in the early, middle, or in the end of English lesson.Based on the three cycles implemented in applying English- bahasa Indonesia compatibility in writing descriptive text to improve writing skill, most students has gradually improved their achievement in writing the text.
Weather forecasting through mathematical ability

Mathematics plays a fundamental role in forecasting the weather.Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions.Since the advent of supercomputers & weather satellites, forecasting weather has become more accurate.In this article, it is discussed how the advent of mathematics in weather forecasting has made it precise and reliable on a global scale.This paper tries to bring out a best sample to find reliable output with these parameters.The major factors are considered as predictors for the mathematical model formulation.
Scientific machine learning: methods to compute efficient surrogate models from small sets of observations

Design optimization, inverse problems and uncertainty quantification problems are commonly encountered across all the domains of transportation and industrial engineering.Main challenges relate to the complexity and the dimensionality of the simulation-based parametric studies associated with these problems, which frequently require to evaluate expensive numerical models many times (many-query).Within this context, statistical learning offers powerful methods to compute efficient surrogate representations of the expensive models which are commonly used in an offline-online fashion: offline the surrogate representations are computed from a set of evaluations of expensive models, online the surrogates are used for the many-query tasks to contain the costs of the individual model interrogations.An open research question relates to the reliability of the predictions obtained with these methods, in particular when the set of available parametric evaluations is small [1][2][3].This work discusses scientific machine learning methods that build up on the synergies between model order reduction and statistical learning to obtain computationally efficient representations suitable for many-query and time-constrained engineering problems.In particular, projection based methods are used to constrain the learning process and make it amenable when only small sets of expensive evaluations (observations) can be collected.Different types of applications will be discussed with relevance to transportation and naval engineering: from simulation-based design problems to optimal data selection and allocation for system identification problems.
E-KAMUS TEMATIK AJURUMIYAH : MODERNISASI KITAB TRADISIONAL UNTUK SANTRI MILENIAL

This research is motivated by the difficulties of students in understanding the Ajurumiyah book, which is caused by ignorance of the meaning of the vocabulary or terms described in the Jurumiyah book. The limited time in teaching and learning activities at Madrasah Diniyah also affects the level of student's understanding, so the development of an e-dictionary based on an android application is needed to improve the understanding and learning motivation of millennial students. Therefore, the researcher offers a solution by developing a thematic e-dictionary for the vocabulary of the Ajurumiyah book for students of Madrasah Diniyah with the ADDIE model of R&amp;D research design. This research shows that the needs for development of Ajurumiyah book are form of an application with an attractive design. This e-dictionary can be run on smartphones with the Android operating system without using an internet connection. the validation analysis by Expert and teacher about the product shows that this e-dictionary product is good and worth to use, with score 84.2.
A Decade of Multiple-media Forgery Detection: A Comprehensive Review

The rapid proliferation of digital media and ease of manipulation necessitate robust forgery detection techniques to maintain multimedia trustworthiness.This review paper offers a comprehensive overview of the advancements in forgery detection techniques over the past decade, focusing on traditional, machine learning-based, and deep learning-based approaches.Traditional techniques involve watermarking, signatures, and statistical property analysis, while machine learning-based methods employ supervised learning for automatic forgery classification.Deep learning-based methods utilize convolutional neural networks (CNNs) to learn hierarchical features from raw pixel data, demonstrating exceptional performance in detecting advanced manipulations.Despite these advancements, challenges persist, including limited availability of labeled data, adversarial attacks, generalization across different forgery techniques, and real-time detection.Addressing these challenges is crucial for enhancing the trustworthiness of digital media and preserving the integrity of the digital landscape.This review paper aims to provide a thorough understanding of the current state of multiple-media forgery detection and inspire future research directions to tackle remaining challenges.
Participation In Online Health Communities: Decoding the antecedents and outcomes

A significant number (49 %) of Indian users tend to rely on digital media to either access health-related information or use the internet as a precursor to visit a doctor [20]. According to a report by Raheja [48], "The Internet of Things” (IoT) connection has the potential to change the malfunctioning medical system into a comprehensive, effective, and individualised system. This will allow for a more proactive approach to wellness and overall health, reducing medical expenses through these inclusive practices. Online health communities in India demonstrate significant clout to transform the healthcare industry by empowering patients. They offer a platform to all key stakeholders, that are, the healthcare professionals, patients and even the caregivers alike, to come forth, share their experiences and develop remedies for various issues faced by the healthcare industry. This paper examines the antecedents involved in the empowerment of patients in Online Health Communities (OHCs) and the outcomes of this in the form of participant compliance. The research additionally examines the moderating impact of certain factors such as the patients’ e-health literacy and health locus of control (HLOC) and the physicians’ paternalism, in examining the relationship between patient empowerment and compliance. The findings of the research propose a construct or a theoretical model for the numerous factors and moderators associated with the patient’s participation in online health communities. The social support available to patients leads to more empowered patients, ultimately resulting in higher Patient compliance. Further, this patient empowerment, which comprises of sense of autonomy, competence and self-efficacy makes people more compliant. The theoretical construct between patient empowerment and patient compliance, is further moderated by the patients’ e-health literacy and health locus of control (HLOC) and the physicians’ paternalism.
Human-computer Interaction Gesture Control Recognition for High-performance Embedded AI Computing Platform

This article mainly introduces a gesture control recognition method based on embedded AI computing platform, which uses infrared cameras and AI processing units to recognize and track gestures.Using infrared cameras as detection tools can achieve low-cost, easy-to-use and other characteristics.In this algorithm, firstly, the Gesture recognition method based on color model is used to convert the color of the input gesture image and extract the corresponding skin color features; Then, by weighting the skin color features, richer color information is obtained; Finally, the recognition of gestures was achieved by calculating the similarity between color features and skin tone features.This article designs a gesture control recognition system based on infrared cameras and AI processing units.This article adopts a deep learning based object detection algorithm.The algorithm uses Convolutional Neural Network (CNN) to detect the target, and realizes Gesture recognition and tracking without increasing the hardware cost.In the training process, CNN is used to extract the color features and color information of gesture images for training, while in the testing process, infrared cameras of the same number and size are used to test the trained model.In addition, in order to achieve the characteristics of low cost and ease of use, this paper uses the target detection algorithm based on deep learning to verify the results of Gesture recognition.
Multi-task control of swarm robot with double integral model

Biological individuals in the wild often have a definite size and mass, so simulation of the real biological swarm behavior should take these factors into account. The article focuses on building an algorithm to calculate the force acting on each individual robot in the swarm based on their mass through the “Double integral” model. When the robots are required to perform multi-tasks simultaneously, the priority order of tasks must be classified, and the task with lower priority will be projected into the Null space of the higher priority tasks. Each individual robot in the swarm has to fulfill following three tasks: avoid obstacles, move to the goal, maintain the swarm. In this study, the author chooses the priority level in the following order: avoiding obstacles, moving to the goal and finally maintaining the swarm. With an assumption that the obstacles are fixed and known in advance. Finally, the theoretical studies are simulated and verified by Matlab software.
Reflections On The Right To The City

The notion of the right to the city comes from the French Marcel François Lefebvre but, if I wanted a one sentence quote, I would prefer David Harvey’s, which states that the ‘right to the city is the right to remake, to transform the city and be transformed in it by ourselves’. In return of that how do we make the city and how does the city remake us? In my opening keynote I want to talk about some of the responses to the pandemic but also about some of the threats that loom on the horizon in the post-pandemic city. Doing so will hopefully set the stage for conversations about possible interventions, and where do we choose our points of leverage in which to remake the city.
Post-Pandemic Heritage. Game Design Tools For An Engaging Experience

Digitisation of heritage and the creation of open digital archive contribute in a large scale dissemination of history and culture. Today, technology allows us not only to easily access information on heritage and view 3D models of buildings or artefacts, but also to experience virtually historic sites or historic object, by representing them accurately, either referring to the recreation of exiting objects or rebuilding models of objects as they may have appeared in the past (Roussou, 2002). Moreover, digital heritage platforms allow visitors to navigate through virtual spaces and access information data, but still provide a low level of interactivity and immersion (Champion, 2008). By providing simple navigation they offer passive interaction and less engagement, while affords to navigate and have inputs to which to respond offer a more active mental interaction. However, what is missing is dynamic involvement of users and an engaging narrative. Although, digital models today are becoming more and more realistic representations of the physical objects and in general of the environment, they still lack a narrative which describes the intangible elements of cultural heritage, human attitude in this environment and simulates the cultural context. In order to guarantee an effective engagement with learning experience and offer better entertainment to a larger audience, other than conservators, historians and archaeologists, this study will introduce an interactive and immersive approach in heritage 3D platforms based on game design tools. The paper will explore frst the state of the art on virtual heritage worldwide and in particular applications in the case of Albanian heritage, highlighting the level of interactivity and user engagement. Then, interactive tools from video games will be analysed and discussed in view of their incorporation in virtual heritage platforms or serious games to make the dissemination of virtual heritage more interactive and immersive. In this sense, this study will discuss how to develop interactive narrative for historical heritage based on interactivity and narration elements, how to develop an interactive story space and introduce player character as a view point in heritage exploration.