Reviewerly/exHarmony huggingface dataset:
Online Decentralized Multi-Agents Meta-Learning With Byzantine Resiliency

Meta-learning is a learning-to-learn paradigm that leverages past learning experiences for quick adaptation to new learning tasks. It has a wide application, such as in few-shot learning, reinforcement learning, neural architecture search, federated learning, etc. It has been extended to the online learning setting where task data distribution arrives sequentially. This provides continuous lifelong learning. However, in the online meta-learning setting, a single agent has to learn many varieties of related tasks. Yet, a single agent is limited to its local task data and must collaborate with neighboring agents to improve its learning performance. Therefore, online decentralized meta-learning algorithms are designed to allow an agent to collaborate with neighboring agents in order to improve learning performance. Despite their advantages, online decentralized meta-learning algorithms are susceptible to Byzantine attacks caused by the diffusion of poisonous information from unidentifiable Byzantine agents in the network. This is a serious problem where normal agents are unable to learn and convergence to the global meta-initializer is thwarted. State-of-the-art algorithms, such as BRIDGE, designed to provide robustness against Byzantine attacks are slow and cannot work in online learning settings. Therefore, we propose an online decentralized meta-learning algorithm that works with two Byzantine-resilient aggregation techniques, which are modified coordinate-wise screening and centerpoint aggregation. The proposed algorithm provides faster convergence speed and guarantees both resiliency and continuous lifelong learning. Our simulation results show that the proposed algorithm performs better than state-of-the-art algorithms.
Agile Project Management: Evaluation of Implementation Barriers Using the BWM

As today’s business environment is highly competitive and dynamic, APM is seen as a winning project management methodology to be adopted, especially in the IT business. However, with the popularization of APM, new challenges emerge. This study identifies the barriers in APM implementation and obtains relative rankings for their importance using the input of industry experts and Multi-Criteria Decision-Making (MCDM) techniques. Following the thorough review of the literature and five individual interview sessions, a total of 13 barriers of APM were identified and categorized under 4 main themes (High-level Organizational, project planning, team operation, and Quality Management). The final weights of the 13 APM barriers were obtained with the application of a recently introduced MCDM technique called Best–Worst Method (BWM). The findings from this study reveal that the top five sub-barriers are in order of priority: the lack of adequate and accurate description of tasks, the unclear time requirements and project schedule, the lack of effective communication/knowledge sharing, the lack of clarity in team roles and task ownership and the lack of understanding of agile project management principles. This reflects the fact that barriers are spread out into a wide range of factors, highly dependent on the inherent features of APM as well as on human communication and social skills.
A MDPs-Based Dynamic Path Planning in Unknown Environments for Hopping Locomotion

Hopping robots, or "hoppers", are promising explorers capable of navigating rough terrain such as disaster areas and celestial environments. For example, rovers exploring planetary surfaces need to minimise the risk of failure and maximise the acquisition of information about their environment. Hopping locomotion in such environments is inherently uncertain because the details of the environment are largely unknown. As a result, path planning algorithms must account for these uncertainties in order to effectively traverse these environments. This study presents a novel hopping path planning algorithm for uncertain environments. The proposed algorithm uses Markov Decision Processes (MDPs) to compute motion uncertainties and subsequently generates optimal actions for all states according to the terrain conditions and mission requirements. In addition, the proposed algorithm incorporates a perception method using hopping locomotion features, which enables dynamic path generation. The proposed algorithm is evaluated through simulations in three different environments, which demonstrate that the hopper can achieve its goals with a 98% success rate on hard ground and heterogeneous terrain, and over 80% success rate on sandy terrain, using the proposed algorithm. Furthermore, the robustness of the proposed algorithm is validated through comparison with a greedy algorithm using the same payoff function, which shows that the proposed algorithm achieves 20 times higher success rate and 38.7% lower average number of steps than the greedy algorithm in the best case scenario.
The Interim Characteristics of Large-Scale Online Education at Chinese Institutions of Higher Education—An Empirical Study Based on a Questionnaire Survey of Students, Instructors, and Administrative Personnel

In order to fully comprehend the state of online education at institutions of higher education in China during the 2020 COVID-19 pandemic, the Online Education Task Group of the Center for Teaching and Learning Development at Xiamen University conducted a questionnaire survey of the state of online education, and a total of 334 institutions of higher education, 13,997 instructors, and 256,504 students participated in this survey. The results of the survey showed that large-scale online education in China met or even exceeded the expected goals for the first time, in a successful round of online educational practices, with important value as a reference for informatization construction at institutions of higher education in the post-pandemic era and the achievement of hybrid online-offline education. However, due to being “compelled” by the pandemic, this round of large-scale online education left behind many interim characteristics of this specific period of time in a number of areas, including the preparation of instructors and students for online education, support by education platforms, the technical services of education platforms, online services provided by schools, online education models and characteristics, outcomes in online education, the primary factors influencing outcomes in online education, existing problems in online education, the challenges faced by instructors and students in online education, and recommendations by instructors and students for improvements to online education. From a long-term perspective, these interim characteristics leave room for a series of reflections on subsequently continuing to deepen educational reforms. In the face of these interim characteristics having overtones of an “emergency response,” from the perspective of informatization construction in education in the post-pandemic era, this article proposes five relationships that must be properly handled: the relationship between the resources allocated to online education by programs and by the market; the relationship between hard development and educational concepts; the relationship between equity and efficiency; the relationships between different agents’ understandings and practices; and the relationship between domestic practices and international experiences.
BELAJAR SAMBIL BERMAIN KOSA KATA BAHASA INGGRIS PADA SISWA SEKOLAH DASAR: EVALUASI MOTIVASI DAN PENGETAHUAN

Foreign language skills are needed to be able to compete internationally, so the role of English in education is expected to improve the quality of human resources as one of the pillars of education. Improving the quality of human resources is meant to start earlier, namely for the elementary school students. The Community Service (PkM) activities at elementary schools in central Maluku aims to (a) find out students' motivation in learning English, and (b) find out English knowledge at the elementary school level at the level of words and themes that become students' interest in learning. The results of PkM activities by applying learning while playing methods: word play, short sentences, and sentence dictation (Indonesian sentences, written in English and vice versa) indicate that the majority of students have motivation in learning English, it can be proven through the active role of students in the teaching and learning process and students' enthusiasm in answering questions given by the teacher. While student’s knowledge of English shows that the number of students who could fill in the words in the table according to the number given (50 words in 15-20 minutes) was very minimum (4 pairs from a total of 20 pairs or 20%). Further, students could write words with varied topics/themes. The most popular topics were animals (animals) and colors (colors), and also students were able to write simple Indonesian sentences into English or vice versa.
Near Term Hybrid Quantum Computing Solution to the Matrix Riccati Equations

The well-known Riccati differential equations play a key role in many fields, including problems in protein folding, control and stabilization, stochastic control, and cybersecurity (risk analysis and malware propagation). Quantum computer algorithms have the potential to implement faster approximate solutions to the Riccati equations compared with strictly classical algorithms. While systems with many qubits are still under development, there is significant interest in developing algorithms for near-term quantum computers to determine their accuracy and limitations. In this paper, we propose a hybrid quantum-classical algorithm, the Matrix Riccati Solver (MRS). This approach uses a transformation of variables to turn a set of nonlinear differential equation into a set of approximate linear differential equations (i.e., second order non-constant coefficients) which can in turn be solved using a version of the Harrow-Hassidim-Lloyd (HHL) quantum algorithm for the case of Hermitian matrices. We implement this approach using the Qiskit language and compute near-term results using a 4 qubit IBM Q System quantum computer. Comparisons with classical results and areas for future research are discussed.
Analisis Kemampuan Penalaran Siswa Melalui Media Calculate Your Path

The purpose of this study was to determine the reasoning ability of students in solving the problem of summing up the AL-Mudzakir Kisaran Integrated PAUD students.This research method is descriptive qualitative.The subjects in this study were 21 students of the Integrated PAUD Al-Mudzakir Kisaran.The data collection technique used is a test designed in the calculate your path media.The results of data analysis showed that the category of students as much as 15.4%, the reasoning ability of students in the medium category there were 14. students as many as 65.6%, the reasoning ability of students in the low category there were 4 students as much as 19%.From the data analysis, the percentage of the average value of the achievement of students' reasoning abilities is 61%.so it can be concluded that the reasoning ability of Al-Mudzakir Integrated PAUD students is in the medium category.
Decoherence quantification through commutation relations decay for open quantum harmonic oscillators

This paper is concerned with multimode open quantum harmonic oscillators (OQHOs) described by linear quantum stochastic differential equations with multichannel external bosonic fields. We consider the exponentially fast decay in the two-point commutator matrix of the system variables as a manifestation of quantum decoherence. Such dissipative effects are caused by the interaction of the system with its environment and lead to a loss of specific features of the unitary evolution which the system would have in the case of isolated dynamics. These features provide nonclassical resources exploited in quantum computation and quantum information processing technologies. We discuss a system-theoretic definition of decoherence time in terms of the commutator matrix decay and obtain an upper bound for it using algebraic Lyapunov inequalities. Employing spectrum perturbation techniques, we investigate the asymptotic behaviour of a related Lyapunov exponent for the oscillator when the system-field coupling is specified by a small coupling strength parameter and a given coupling shape matrix. The invariant quantum state of the system, driven by vacuum fields, in the weak-coupling limit is also studied. We illustrate the results for one- and two-mode oscillators with multichannel external fields and outline their application to a decoherence control problem for a feedback interconnection of multimode OQHOs.
A general mobile manipulator automation framework for flexible tasks in controlled environments

To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible automation. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by the mobile base’s parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base’s location, environment information, end-effector’s path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose’s bias to modify the end-effector’s path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the parking precision. For the released code, please contact [email protected].
An Open Data platform for decision making in local public administration

This paper presents the Milan Open Data (OD) platform as a means to provide statistics and data in the framework of “Data-Driven Milan”, a city where policy decisions are taken in an “informed and aware” way using data. Open Data are ever more important in providing citizen communities with useful information. Over the last 10 years, the municipality of Milan has developed its OD platform from an experimental portal to a fully-fledged portal with more than 1,600 datasets, implemented a Linked Open Data (LOD) system and 8 advanced data visualization projects, and produced OD policies and operating guidelines. The OD portal is based on the CKAN technology, which makes datasets available via both download and Application Programming Interfaces. It currently has about 9,000 unique visitors per month. Data are also published as tables on the statistical portal and maps on the geo-portal. A single entry point to the three portals is about to be released. The advanced visualization projects make data available as reader-friendly graphics. Depending on its topic, a project might be a storytelling of the city through key statistics, a means to communicate the administration’s achievements, or a “data democratization” operation, such as “Open bilancio”, which publishes detailed information about the municipality’s annual budget. LOD are semantically enriched and machine-readable data that help to implement data interoperability between distributed systems. The ongoing LOD automation project aims to improve the current system by minimizing manual operations in the dataset lifecycle. Three Municipal Resolutions have been issued to define OD governance: the first establishes that data are released under an Open License, to promote the digital economy; the second promotes data interoperability and quality; and the third, issued in 2021, establishes the guidelines and an operating model for OD governance, including ontology-supported data publishing.
Framing Emotions on Mainstream Media Instagram Feeds During the First Wave of the COVID-19 Pandemic

The main goal of this paper is to answer the following questions. 1) What visual narratives about emotions are constructed and imposed by mainstream media on their Instagram channels during the COVID-19 pandemic? 2) How do they frame them by complementing existing or creating new iconic-symbolic representations of the semantic framework of selected emotions (joy, fear, sadness, anger)? The selected visual material included photos and drawings and was examined using quantitative and qualitative content analysis and comparative semiological analysis. The analysis showed a low saturation of media messages with emotions and made it possible to describe the semantic media framework of individual emotions according to the following scheme: bodily sensations, expressive gestures, social situation and emotional culture.
Coping with Randomness in Highly Complex Sys-tems Using the Example of Quantum-Inspired Traffic Flow Optimization

Developing new solutions to complicated large-scale problems typically requires large-scale numerical simulation. Therefore, traffic simulations often run against randomized simulations instead of real-world traffic situations. This paper demonstrates a method to calculate the statistical significance of numerical simulations and optimizations in the presence of numerous random variables in complex systems using one-sided paired t-tests. While the paper covers a specific Fujitsu traffic-optimization project which uses SUMO for simulating the traffic situation, the method can be applied to many similar projects where a complete investigation of the solution space is not feasible due to the size of the solution space.
SUMO Simulations for Federated Learning in Communicating Autonomous Vehicles

In transportation, a vehicle's route is one of the most private information. However, to mutually learn some phenomena in a city, for example, parking lot occupancies, we might have to reveal information about it. In this paper, we focus on assessing the privacy loss in a vehicular federated machine learning system. For the analysis, we used the Monaco SUMO Traffic Scenario (MoST). We also used the simulation inputs as statistical data to calculate privacy loss metrics. Results show that a vehicular federated machine learning system may pose a smaller privacy threat than individual learning, but its performance is lower compared to a centralized learning approach.&#x0D; Due to the vast amount of data and processing time, we also describe a method to build a Docker image of SUMO together with a software client-server architecture for SUMO-based learning systems on multiple computers.
Formation of professional identity among master’s degree students in pedagogy with system-modular organization of educational process

Current geopolitical situation and Russia's withdrawal from the Bologna process showed the urgent need to improve the national system of continuous pedagogical education, in which the master's program as the second level of higher education is focused on more in-depth, in comparison with the bachelor's degree, professional training of graduates, providing them with a high level of professional identity as the most important indicator of professional development. Due to a number of reasons (changes in master's degree standards, increased requirements for graduates with short terms of study and the lack of basic specialized education for many students, etc.), the quality of education in the master's program has significantly decreased, manifested in the absence of an increase in the educational level of students. Currently, to ensure the quality of teacher training in master’s studies, universities should find reserves in the organization of the learning process to develop professionalism among students, develop and implement flexible modular educational programs, their technological support, and build an effective system of interaction with employers. The purpose of the study: to develop a model for the formation of professional identity of students of pedagogical magistracy in the conditions of a system-modular organization of the educational process. The theoretical methods were an analytical review of scientific sources on the problem of professional identity, analysis and generalization of the results; pedagogical modeling. Based on the results of the theoretical analysis, the structural-content components of the professional identity of teachers are revealed as a complex, integrative, dynamic personal education that influences the professional development and success of the professional pedagogical activity. The method of pedagogical modeling led to the creation of a model for the formation of professional identity among master’s degree students in pedagogy with a system-modular organization of the educational process. The model makes it possible to form ideas about the formation of students' professional identity during the period of study in a pedagogical master's program as a systemic, dynamic, cyclical process of subject-subject pedagogical interaction between participants in educational relations.
AI based Transformation on Real Banking System 4.0

The comprehensive banking management system encompasses a three-tiered approach, with separate modules for administrators, bank employees, and clients. The back-end is constructed using Java Spring Boot with a layered architecture, while the front-end is built using React JS and features React Redux for state management. Token-based authentication and JPA with Hibernate as the ORM framework ensure the security and reliability of the system. An AI chatbot has been incorporated into the website to provide customer support, and a system for paperless withdrawals and deposits types transactions has been implemented to speed up banking processes. The project has determined the methodology and algorithm for introducing virtual debit cards and card- less ATM transactions for the convenience and security of customers. Additionally, the project aims to integrate data analysis into the system using AI models to analyze user spending behavior, predict efficient ATM installation locations, and implement bio- metric E-Verification processes. The focus on user‐friendliness and efficiency reflects the projects goal of providing a comprehensive solution for all aspects of banking management for all parties involved.
A Novel Thanka Image Inpainting Method with Euler’s Elastica and Iterative Denoising and Backward Projections

This paper presents a brand-new Thanka picture inpainting technique based on Euler’s elastica, iterative denoising, and backward projections (EEIDBP). Specifically, a model of Euler’s elastica is introduced to estimate the original observation due to its lower staircasing effects and better approximation of natural images. A method for backward projection and iterative denoising is applied to achieve a more accurate estimate of the original signal by alternating iterations between the estimation of the original signal and the estimation of the original observation. The experimental findings demonstrate that, in terms of a subjective assessment, the quantitative peak signal-to-noise ratio (PSNR), and the structural similarity (SSIM), the proposed technique outperforms the state-of-the-art picture inpainting methods.