Reviewerly/exHarmony huggingface dataset:
Training a robot with limited computing resources to crawl using reinforcement learning

In recent years, new successes in artificial intelligence and machine learning have been continuously achieved. However, this progress is largely based on the use of simulations as well as numerous powerful computers. Due to the volume taken up and the necessary power to run these components, this is not feasible for mobile robotics. Nevertheless, the use of machine learning in mobile robots is desirable in order to adapt to unknown or changing environmental conditions.This paper evaluates the performance of different reinforcement learning methods on a physical robot platform. This robot has an arm with two degrees of freedom that can be used to move across a surface. The goal is to learn the correct motion sequence of the arm to move the robot. The focus here is exclusively on using the robot’s onboard computer, a Raspberry Pi 4 Model B. To learn forward motion, Value Iteration and variants of Q-learning from the field of reinforcement learning are used.It is shown that since the structure of some problems can be described by a very limited problem space, even when using a physical robot relatively simple algorithms can yield sufficient learning results. Furthermore, hardware limitations may prevent using more complex algorithms.
Towards Gesture-based Cooperation with Cargo Handling Unmanned Aerial Vehicles: A Conceptual Approach

This work provides the fundament for a gesture-based interaction system between cargo-handling unmanned aerial vehicles (UAVs) and ground personnel. The system has platforms with increasing levels of automation in mind and enables operators to visually communicate commands with higher abstractions through a minimum number of necessary gestures. The UAV supports the operator in monitoring surroundings and provides visual feedback in order to increase safety while carrying cargo near ground-level. The interaction concept intends to transfer two goal-directed control techniques to a cargo-handling use case: Object selection via deictic pointing and a proxy manipulation gesture are used to visually communicate intention and control the UAV’s flight. A visual processing pipeline to realize this challenge is presented along with first simulated evaluations of subcomponents.
On Path Regression with Extreme Learning and the Linear Configuration Space

This paper studies the path regression problem, that is learning motion planning functions that render trajectories from initial to end robot configurations in a single forward pass. To this end, we have studied the path regression problem using the linear transition in the configuration space and shallow neural schemes based on Extreme Learning Machines. Our computational experiments involving a relevant and diverse set of 6-DOF robot trajectories have shown path regression’s feasibility and practical efficiency with attractive generalization performance in out-of-sample observations. In particular, we show that it is possible to learn neural policies for path regression in about 10 ms. - 31 ms. and achieving 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> – 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> Mean Squared Error on unseen out-of-sample scenarios. We believe our approach has the potential to explore efficient algorithms for learning-based motion planning.
Human-Aware Waypoint Planner for Mobile Robot in Indoor Environments

As the utilization of robots in indoor environments increases, it has become common for humans and robots to co-exist in such environments. Most human-aware navigation algorithms only considered humans in the robot's field of view. However, in cases of L-shape corridors, there is a high possibility that human suddenly appears. To deal with this situation, we propose an improved corner detection algorithm and a novel waypoint planner, WPC. The proposed algorithm is validated through simulations using PedSim and Gazebo.
A Novel Hybrid Mechanism for Generation of Pseudo-random Sequences for Data Protection Purposes

In this paper, a mechanism for the generation of pseudo-random numbers with a very high level of randomness is proposed. The design of this mechanism is based on geometric structures simulated by software, specifically rotating cylinders that are activated, also, with random inputs produced by means of events of the electronic system on which it operates. Due to the random quality, its outputs can be used to implement robust systems for data protection.
Personalization of learning in programming on the example of the application of internet services

The article deals with the concept of personalization of learning, actualizes the need to develop a model of the personalized learning in the field of vocational education in the context of the implementation of the National Program “Digital Economy”. Existing learning models created on the basis of a personalized approach are analyzed. A comparative analysis of the personalized, differentiated, individualized and adaptive learning is given. A model of personalization of learning in the study of programming through Internet services has been developed. The practical significance of the study lies in the possibility of introducing a model of personalization of learning when organizing programming classes in professional educational institutions. The results of the study show that a personalized learning model provides the student with the right to choose, the route of movement within the subject, the opportunity to go ahead and properly manage the time won, or, conversely, return, and catch up with classmates. This approach allows the learner to “drive” the learning car rather than sit in the passenger seat. This is the main motivating factor. The conclusions formulated in the article are of practical value for heads of educational organizations, developers of educational materials, methodologists of educational institutions to build an educational process using a personalized approach to a relatively new trend in developing vocational education systems.
A corrective approach to assessing students’ academic achievements in LMS Moodle

The article proposes a large-scale implementation of the LMS Moodle learning environment in the distance learning process and recommends using this resource for in-person learning. We believe that the knowledge assessment system designed for distance learning cannot be automatically applied to assessing students taught in other educational modes; it must be adapted to them. The research aimed to study the assessment issues in the Moodle environment and to develop a methodology for using it to assess the knowledge of Mathematics acquired by students doing their economic bachelor’s degree. To achieve the goal, we studied scientific literature on this subject and employed pedagogical design, experiment, and student survey as research methods. As a result, we designed a monitoring and measuring system to handle mathematical knowledge assessment in LMS Moodle. In addition, we developed a procedure for mid-term assessment using this system. We believe that teachers should verify electronic testing results when evaluating students’ academic achievements in the Moodle environment. The article describes the experimental results confirming that teacher-adjusted grades show students’ knowledge level more objectively. The novelty of the study consists in developing a corrective methodology for utilizing the LMS Moodle to assess students’ knowledge of mathematics in the full-time in-person learning mode. The proposed approach can be used to assess students’ knowledge in other academic disciplines. Moreover, it can be integrated into the digital learning environment of educational institutions.
Multifunctional and flexible online platforms for creating educational materials

The article actualizes the need to use multifunctional flexible online platforms to promote educational activities, in particular the discipline “Machine Learning”. The main characteristic features of the discipline “Machine Learning” are described, the teaching of which consists in a task-based approach through writing program codes in a programming language, which is the Python 3 interpreter with a bundle of libraries selected: NumPy, Pandas, Matplotlib and Seaborn for data processing and visualization. The Scikit-learn library is used directly for machine learning. In addition to the Python 3 interpreter, coding tools are involved, namely: the PyCharm Community cross-platform development environment and the Jupyter Notebook open source web application. The potential of educational multifunctional flexible online platforms including designers of open online courses to facilitate independent learning of students is evaluated. According to the versions of various domestic and foreign scientific publications, the most mentioned online platforms are identified, their functionality regarding the placement of material in the fields of programming and machine learning was analyzed. Based on the analysis of the functional, a group of potential basic requirements for educational platforms in teaching programming within the discipline “Machine Learning” was identified, analyzed and discussed.
Pedagogical technology for solving competence problems in a digital environment

The article is aimed at substantiation of peculiarities and stages of pedagogical technology for solving competence problems, implemented in the digital educational space. The technology based on the environmental methodological approach (the educational environment as a means of educational activity). Such features of competency-based tasks as non-formalization, information redundancy, involvement of practical and professional situations are correlated with digital environment opportunities (multiple choice of learning options, personalized activities, digital tools). The technology is presented as non-linear with integrated goals, tools and outcomes that are achievable in a digital space. The article justifies importance of competence tasks solution in the digital environment for the quality of professional training with new ingredients of student’s competences (digital skills, soft skills). The structural unit of the technology is the digital microenvironment of the educational task. It presents the purpose and content of the task with digital pedagogical resources providing student’s activity. Pedagogical technology is built as a set of digital microenvironments of educational tasks aimed at solving the complex competence problem. Digital pedagogical resources of microenvironment, which organize self-directional activity of students, are resources of activity management, digital learning resources and network communications resources. The main stages of the teacher’s activity in environmental technology are substantiated. The practical relevance of technology demonstrated by its implementation in the future teachers training. The article is addressed to university teachers, focused on achieving new professional training results by means of digital environment. The presented scientific approaches are new for domestic and foreign pedagogical practice.
Problems of informatization of rural schools within the framework of the National Project “Education”

The education system is a significant component of the socio-economic development of any state, since it directly affects the development and formation of human capital. The special role of the school in rural areas is not one of just an educational organization, it is primarily an “anchor” for the preservation of rural settlements, a symbol that the village has prospects and a future. The purpose of the presented study is to assess the state of information and communication technologies (ICT) in rural schools, taking into account the impact of the National Project (NP) “Education”. The main research methods were methods of comparative analysis, data comparison, exponential smoothing. As a result of the conducted research, a forecast was made of the number of students in the general education system in rural areas and school institutions in rural areas at the beginning of the 2024/2025 academic year. The forecast revealed the risk of not achieving the targets of NP “Education” in rural areas, including in the field of ICT. The practical significance of the research results lies in the fact that the obtained forecast results allow us to take a number of necessary measures to prevent possible risks of not achieving the indicators. It is necessary to review the mechanisms for assessing the effectiveness and achievability of the indicators laid down in the NP, increase funding and the pace of creating new school places in rural areas, increase the availability of modern information resources and equipment.
Dear Nationalists: Writing Poetry in (Anti) Social Media

Drawing from my experience of being gender trolled for my poem, titled “Dear Nationalists”, shared on Facebook and subsequently published in a webzine, I explore the relationship between Northeast Indian Anglophone writings and the role played by language in its reception and the affect elucidated by it. Toward this end, I explore the relationship of English to other local languages/mother tongue to understand its emotive appeal. NIAL are already read as a mark of disloyalty to the mother tongue or at least invites an opinion of lacking rootedness to one’s native milieu. In this paper I explore whether the adverse reactions to my poem, however disproportionate, can be understood by exploring the reception of a poem, and the role played by gender and cultural identity in the very act of reading.
Secured Access Policy in Ciphertext-Policy Attribute-Based Encryption for Cloud Environment

The cloud allows clients to store and share data.Depending on the user's needs, it is imperative to design an effective access control plan to share the information only with approved users.The user loses control of their data when the data is outsourced to the cloud.Therefore, access control mechanisms will become a significant challenging problem.The Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an essential solution in which the user can control data access.CP-ABE encrypts the data under a limited access policy after the user sets some access policies.The user can decrypt the data if they satisfy the limited access policy.Although CP-ABE is an effective access control program, the privacy of the policy might be compromised by the attackers.Namely, the attackers can gather important information from plain text policy.To address this issue, the SHA-512 algorithm is presented to create a hash code for the user's attributes in this paper.Depending on the created hash codes, an access policy will be formed.It leads to protecting the access policy against attacks.The effectiveness of the proposed scheme is assessed based on decryption time, private key generation time, ciphertext generation time, and data verification time.
Intelligent Digital Envelope for Distributed Cloud-Based Big Data Security

Cloud computing offers numerous web-based services.The adoption of many Cloud applications has been hindered by concerns about data security and privacy.Cloud service providers' access to private information raises more security issues.In addition, Cloud computing is incompatible with several industries, including finance and government.Public-key cryptography is frequently cited as a significant advancement in cryptography.In contrast, the Digital Envelope that will be used combines symmetric and asymmetric methods to secure sensitive data.This study aims to design a Digital Envelope for distributed Cloud-based large data security using public-key cryptography.Through strategic design, the hybrid Envelope model adequately supports enterprises delivering routine customer services via independent multi-sourced entities.Both the Cloud service provider and the consumer benefit from the proposed scheme since it results in more resilient and secure services.The suggested approach employs a secret version of the distributed equation to ensure the highest level of security and confidentiality for large amounts of data.Based on the proposed scheme, a Digital Envelope application is developed which prohibits Cloud service providers from directly accessing insufficient or encrypted data.
Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient's health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features.However, choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning (AL), Convolutional Neural Network (CNN), BiGRU, and Multi-Attention, called ACBMA in this research, is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text's global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network (RNN) gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method, achieve comparable results to state-of-arts methods in sentiment detection, and outperform them with accurate benchmarks.Finally, several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.
Enhanced Best Fit Algorithm for Merging Small Files

In the Big Data era, numerous sources and environments generate massive amounts of data.This enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future changes.Hadoop is used to process this kind of data.It is known to handle vast volumes of data more efficiently than tiny amounts, which results in inefficiency in the framework.This study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm (EBFM) that merges files depending on predefined parameters (type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same range.Its primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established system.This procedure takes place before the files are available for the Hadoop framework.Additionally, the files generated by the system are named with specific keywords to ensure there is no data loss (file overwrite).The proposed approach guarantees the generation of the fewest possible large files, which reduces the input/output memory burden and corresponds to the Hadoop framework's effectiveness.The findings show that the proposed technique enhances the framework's performance by approximately 64% while comparing all other potential performance-impairing variables.The proposed approach is implementable in any environment that uses the Hadoop framework, not limited to smart cities, real-time data analysis, etc.
Security Requirement Recommendation Method Using Case-Based Reasoning to Prevent Advanced Persistent Threats

As the world becomes digitized and connected, cyberattacks and security issues have been steadily increasing. In particular, advanced persistent threats (APTs) are actors who perform various complex attacks over the long term to achieve their purpose. These attacks involve more planning and intelligence than typical cyberattacks. Many studies have investigated APT detection and defense methods; however, studies on security requirements that focus on non-technical factors and prevention are relatively few. Therefore, this study aims to provide attack information to users obtained by analyzing attack scenarios as well as security requirements to help the users understand and make decisions. To this end, we propose a method for extracting attack elements by providing users with templates for attack scenarios with different levels of abstraction. In addition, we use a problem domain ontology that is based on the concept of a case to provide users with attack analysis results and recommended security requirements. Our method uses case-based reasoning to retrieve similar cases, recommend reusable security requirements, and propose revision directions. The ontology can be improved by adding the solution to the problem as a new case. We conducted case studies and surveys to evaluate our methods and showed that they help specify security requirements.
Blockchain based integrity assurance framework for <scp>COVID</scp>‐19 information management &amp; decision making at National Command Operation Center, Pakistan

Summary The uncontrollable spread of contagious disease COVID‐19 is a perennial threat to mankind and has resulted in an unprecedented lockdowns in several countries including Pakistan which in turn has caused an adverse socio‐economic impact to all industries. The strategic leadership and concerned state authorities are trying hard to combat and control the spread of COVID‐19 pandemic. The effective use of Information Management &amp; Decision Support (IMDS) System can play significant role in combating pandemic and its spread, managing relief actions effectively, accessing vulnerable communities to roll out targeted subsidies by ensuring the coordinated effort and subsequent implementation. Reliable information is significantly critical to assist government and public health agencies in determining the best way forward to control this global health emergency. Therefore, this paper aims to strengthen capacity of IMDS System used by government institutions and authorities for decision making and information dissemination. In this research work, we addressed the integrity‐based issues that include completeness, correctness, and freshness of data by proposing a block chain‐based integrity protection mechanism. The proposed novel framework is a cascaded formulation of Integrity Assurance (IA) Protocol, Cryptographic Merkle Hash Tree, Digital Signature, and Blockchain. Beside cascaded formulation, two (2) schemes for MHT Generation are also presented in the framework. The proposed framework ensures fairness, completeness, and correctness of data that will be very helpful for secure data management, integration, and utilization in analysis for decision‐making. The proposed framework achieved an accuracy of more than 98.09% with better quantitative performance in standard evaluation parameters.
Development of a Three-Mobile-Robot System for Cooperative Transportation

Abstract The transportation of large-scale objects in a narrow space is a challenging, but useful application for mobile robots. We have developed a three-mobile-robot system to cooperatively lift, support, and transport a large object on a mobile robot system. To facilitate the manipulation involved with loading an object onto the robots, where the robots must maintain firm contact with the object and anchor at its location, we designed an adaptable mechanism for the object-loading platform and a liftable brake to ensure that the robot remains stationary when necessary. Furthermore, each robot is designed to act as an omnidirectional wheel; therefore, all three robots can work as an omnidirectional block when collectively loading an object. The kinematic constraints of the object–robot system and forward kinematics of the robots’ cooperative motion are proposed, and experiments are conducted to confirm the designed mechanisms and confirm that the robots can load an object and cooperatively transport it along the expected trajectory.