Reviewerly/exHarmony huggingface dataset:
Inventing a new Educational System

This chapter presents a totally new idea to create an educational system capable of facing the many challenges of the knowledge age while meeting the needs of tomorrow’s societies, particularly the young men and women who are forced to transform their attitudes and living conditions as times change. While societal change was hardly noticeable during the agricultural age, it accelerated gradually during the industrial age, and became extremely hard to follow during the current knowledge age. Due to this change, a need was created for building an unconventional education system with a built-in capacity to transform itself to maintain creativity and adaptability to changing times, new scientific facts, and unprecedented technological innovations. The transition from one civilization to another creates a new society, having its unique culture, economy, and social, political, and economic structures. All of which dictate that people change their views, attitudes, and patterns of life.
Balancing Information Preservation and Data Volume Reduction: Adaptive Flow Aggregation in Flow Metering Systems

The critical role of network traffic measurement and analysis extends across a range of network operations, ensuring quality of service, security, and efficient resource management. Despite the ubiquity of flow-level measurement, the escalating size of flow entries presents significant scalability issues. This study explores the implications of adaptive gradual flow aggre- gation, a solution devised to mitigate these challenges, on flow information distortion. The investigation maintains flow records in buffers of varying aggregation levels, iteratively adjusted based on the changing traffic load mirrored in CPU and memory utilization. Findings underscore the efficiency of adaptive gradual flow aggregation, particularly when applied to a specific buffer, yielding an optimal balance between information preservation and memory utilization. The paper highlights the particular significance of this approach in Internet of Things (IoT) and contrasted environments, characterized by stringent resource constraints. Consequently, it casts light on the imperative for adaptability in flow aggregation methods, the impact of these techniques on information distortion, and their influence on network operations. This research offers a foundation for future studies targeting the development of more adaptive and effective flow measurement techniques in diverse and resource-limited network environments.
DEVELOPMENT OF METHODS AND MODELS OF HIGHER EDUCATION INSTITUTIONS' ACTIVITY MANAGEMENT

The issues of development of methods, models and mechanisms for managing the activities of higher education institutions in the conditions of confrontation between Russia and western countries have become increasingly significant. These external influences on public authorities, enterprises, institutions and organizations of the subjects of the Russian Federation cannot be ignored in the development of higher education science and its participation in the assessment of the effectiveness of all economic entities in the region. The development of national institutions of higher education needs new approaches, concepts, methods and models, or a significant transformation of those that are currently used in practice. Aim. The aim of the work is to create a set of mechanisms, methods and models to improve the efficiency of management of higher education institutions and their more competent interaction with public authorities and management bodies, as well as industrial enterprises and organizations located on the territory of the region. Materials and methods. The results of analysis of researches on improvement of methods, models and technologies of management of work of higher educational institutions, as well as enterprises and organizations are used. Results. The expediency of modernization of mechanisms of management of higher education institutions, as well as methods and models used in this process is shown. Conclusion. The scientifically grounded provisions defining the key directions of the development strategy of the higher education institutions' activity management system have been formulated.
Fall Detection Based on Fusion of Passive and Active Acoustic Sensing

Numerous studies have extensively examined the living conditions of older adults who live alone, highlighting falls as the most significant threat to their well-being. Recognizing the critical importance of fall detection for older adults, researchers have conducted numerous investigations over the past decade to develop fall detection systems utilizing various detection methods. In this paper, we further explore the potential of acoustic signals in detecting fall events and propose a novel system called FA-Fall, which fully harnesses acoustic signals for fall detection. Our approach revolves around fusing passive and active acoustic sensing through a pair of audio transceivers. We design a multimodal classification framework that integrates an attention mechanism and an anomaly detection mechanism to capitalize on the complementary and redundancy of passive and active sensing. We implemented the FA-Fall system and carried out extensive experiments to evaluate its performance. The results demonstrate that FA-Fall achieves an impressive overall accuracy of 98.97% under typical environmental conditions. Furthermore, it can still detect fall events with over 90% accuracy in challenging environments characterized by considerable background noise or non-line-of-sight conditions.
Exploring Scale-Aware Features for Real-Time Semantic Segmentation of Street Scenes

Real-time semantic segmentation of street scenes is an essential and challenging task for autonomous driving systems, which needs to achieve both high accuracy and efficiency. Moreover, numerous objects and stuff at different scales in street scenes further increase the difficulty of this task. To address this challenge, we develop a lightweight and high-accuracy network termed Scale-Aware Network (SANet), which aims to selectively aggregate multi-scale features while maintaining high efficiency. In SANet, we first design a Selective Context Encoding (SCE) module, which considers the intrinsic differences of various pixels to selectively encode private contexts for each pixel, thus learning more desirable contextual features while reducing redundancy. With the context embedding in hand, we then design a Selective Feature Fusion (SFF) module to recursively fuses them with multiple features at different levels or scales to generate scale-aware features, where each feature map contains scale-specific information. Extensive experiments on challenging street scene datasets, i.e., Cityscapes and CamVid, illustrate that our SANet achieves a leading trade-off between segmentation accuracy and speed. Concretely, our method yields <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$78.1\%$</tex-math> </inline-formula> mIoU at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$109.0$</tex-math> </inline-formula> FPS on the Cityscapes test set and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$77.2\%$</tex-math> </inline-formula> mIoU at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$250.4$</tex-math> </inline-formula> FPS on the CamVid test set. Code will be available at https://github.com/kaigelee/SANet.
Over-the-Air Computing with Imperfect CSI: Design and Performance Optimization

Over-the-air computing (AirComp) has recently attracted considerable attention as an efficient method of data fusion by integrating uncoded communication transmissions with computation thanks to the signal superposition offered by the multiple access channels. However, appropriate processing is required to neutralize the wireless channel effect. As, internet-of-things (IoT) applications through low-cost devices is the main target of AirComp, perfect availability of channel state information (CSI) is not always practical, there is the need to investigate the effect of imperfect CSI on AirComp. Specifically, we present novel closed-form expressions for tight approximations that can be used to design and evaluate AirComp systems. Furthermore, we design a general optimization framework that takes into account both magnitude and phase errors in the CSI. Finally, a pilot retransmission policy is designed, that offers trade-off between resources cost and the gain in the accuracy of the computations. In order to validate its application, a utility function of the cost of retransmission is introduced, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Retransmission Policy Cost (RPC)</i> , which can incorporate the power or throughput cost opposing to the expected gain of the selected policy. Simulations show the deterioration caused by the imperfect CSI and highlight the added value of the proposed policy under various system conditions.
Safe Reinforcement Learning-Based Motion Planning for Functional Mobile Robots Suffering Uncontrollable Mobile Robots

An increasing number of Autonomous Mobile Robots (AMRs) are used in warehouses and factories in recent years. The risk of some of the AMRs being out of control is surging. Although Reinforcement Learning (RL)-based approaches have achieved dramatic success in the motion planning of a large number of AMRs, the available RL-based motion planning approaches cannot provide a safety guarantee for the remaining functional AMRs if some of the AMRs are out of control. To this end, this paper develops a scalable Multi-agent RL (MARL) with Control Barrier Function (CBF)-based shields algorithm. The MARL with CBF-based shields algorithm can address complex high-level tasks by MARL and deal with the safety issue of every single functional AMR by a low-level CBF-based shield. A CBF-based shield is designed for every single functional AMR to ensure that the action of the functional AMR is safe, even if an uncontrollable AMR is pursuing the functional AMR. Experiments are conducted based on simulated warehouse environments to evaluate the effectiveness and scalability of a safe RL-based motion planning approach (The safe RL-based motion planning approach developed in this study is demonstrated in a video: https://youtu.be/I7ja5nFVpY4). developed according to the MARL with CBF-based shields algorithm.
Fostering Educational Development and Innovation in the Global South: Incentivizing Research and Development (R&amp;D)

This paper delved into a comprehensive study to explore the different dynamics surrounding R&amp;D, innovation, and educational development in the diverse regions of the Global South. The research methodology used was a comprehensive literature review that involved the meticulous selection of secondary sources directly aligned with the objectives of the study. The paper embarked on an exploration to investigate the effectiveness of policies and incentives in promoting R&amp;D, the alignment of educational curricula with evolving job markets in the industry, and the pivotal role played by collaboration of various sectors such as educational institutions, industries, and governments in fostering innovation. The study found that policies and incentives can indeed stimulate R&amp;D, but transparency and accessibility are imperative for their success. Curriculum alignment and constant monitoring are therefore vital in meeting the demands of the evolving job market. Collaboration is the cornerstone of innovation, but success hinges on clear governance structures and shared goals. In conclusion, the study offers a strategy for policymakers, educational institutions, industries, and governments in the Global South on how to measures to implement realize increased R&amp;D and innovation. The study recommends prioritizing comprehensive R&amp;D strategies, fostering a culture of innovation, aligning curricula with industry needs, and promoting interdisciplinary education. This is the roadmap through which countries in the Global South can harness the power of education and innovation to achieve sustainable development. Encouraging public-private partnerships and adopting localized interventions are essential steps toward a brighter educational future that shapes the discourse on global educational development and innovation.
Building National Open Science Cloud Initiatives (NOSCIs) in Southeast Europe

The National Initiatives for Open Science in Europe (NI4OS Europe) project supports the development of the European Open Science Cloud (EOSC) by contributing to its portfolio of services, by involving national and regional research communities in the EOSC governance, by strengthening open science (OS) practices, and by promoting the FAIR principles (Macan et al., 2020; Garavelli et al., 2021) to help build the infrastructure and create a favourable environment for open and intensive scholarly communication.The main instrument in achieving this is the network of 15 national Open Science Cloud Initiatives (NOSCIs) established in the partner countries as national-level coalitions of Open Science stakeholders that have a prominent role and interest in the EOSC. The concept of NOSCI has been developed in response to the specific traits and challenges in the targeted region, based on complex and multilayered analyses of stakeholders, policies, and local contexts (Toli et al., 2020). Inclusive by nature, NOSCIs connect stakeholders from across the research lifecycle at the national level and provide not only a testbed for the formulation of OS policies but also a forum for knowledge dissemination and sharing.Drawing on a secondary analysis of the abundant data collected and materials produced during the project, this presentation focuses on the challenges identified as the NOSCIs were built – from data collection in the context of landscaping (Kosanović &amp; Ševkušić, 2019) and policy analysis, through concept development, to implementation, testing, and verification (use cases). It highlights the relationship between individual challenges and NOSCI elements that address these challenges.The challenges are largely owed to diversities within the region, most notably the varying levels of integration into European structures (of the 15 partner countries, eight are not EU members), linguistic diversity, different research governance systems, policy traditions, and available funding. The framework for NOSCI development, the so-called blueprint (Toli et al., 2020), was designed in full recognition of these diversities. It relies on three modular workflows (Toli et al., 2021) and gives maximum flexibility to countries or national initiatives while making sure that all locally specific aspects are addressed. We believe that the approach adopted by the NI4OS-Europe team could be applied in other highlydiversified environments, as has been demonstrated by the NI4OS-Europe use cases, thanks to the flexible mechanism of interaction between challenges and responses underlying the very concept of NOSCI.
Bringing efficiencies to tens of thousands of journals

In addition to the growing number of scholarly journals published by the so-called “big five”, there are tens of thousands of journals that are published by individual scholars or by academic institutions. These smaller operations are a source of great bibliodiversity that deserves to be encouraged but can also be seen as inefficiencies in the system as a whole. The use of a common software—Open Journal Systems (OJS)—is helping these journals take advantage of an economy of scale without needing to centralize or homogenize them. The key to promoting both efficiency and bibliodiversity is in OJS’s open source nature. This presentation will describe the ways in which PKP’s open source software is bringing efficacy to journal operations, to the discovery of their content, and, in the best of cases, to supporting a transformation of the system as a whole.
Construction of suffix tree using key phrases for document using down-top incremental conceptual hierarchical text clustering approach

With development of technologies in the World Wide Web, usage of document increases day by day. In order to access the document easily, document clustering technique is introduced. In the field of data mining, document clustering plays a vital role. Organizing the unstructured and unlabeled document is one of the major problems and it is ever growing and complex. Handling of such unorganized documents causes more expensive. Hence, challenges raised by the continuing growth of unstructured and unlabeled documents are handled in this proposed work. Document clustering is one of the most powerful methods to solve the problem of organizing unstructured documents. There are numerous clustering methods available. In this we were proposed phrase-based clustering algorithm, which is based on the applications of suffix tree document clustering model. The proposed algorithm is designed to use the suffix tree document clustering (STDC) model for accurate representation of document and similarity measurement of the similar documents. This proposed algorithm gives more than 90% accuracy.
Economic Growth, Economic Development, and Poverty: A Bibliometric Analysis

The pertinacious problem of poverty in the world questions whether the existing mechanisms are effective in the poverty reduction process. Exploring this field has intensified since 1989, and this research provides an overview of the theoretical and empirical research that addresses economic development and poverty. Bibliometric analysis is performed, which includes names of the most common authors, journals, and countries of publications, as well as the frequency of words and expressions in the existing literature. Bibliometric units are researched from the Scopus database using keywords "economic development", "economic growth" and "poverty". The main contribution of this analysis is the review of existing literature addressing the key issues and identifying theoretical and empirical research in the field of economic development and poverty. This is the first study of its kind where scientific attention is being paid to economic growth, economic development, and poverty by the most influential journals and most prominent authors in economics.
A data science pipeline synchronisation method for edge-fog-cloud continuum

This paper presents an adaptive data delivery method for data science pipelines. While this method is feasible for processes communicating over any network, in this work we focus on edge-fog-cloud infrastructures. In a diagnostic phase, a model based on the Bernoulli principle is used to create a representation of bottlenecks in a pipeline. In a supervision phase, a watchman/sentinel cooperative system monitors the throughput of the pipeline stages to create a bottleneck-stage scheme. In a rectification phase, this system produces replicas of bottlenecks stages, mitigating the workload congestion using implicit parallelism and load balancing algorithms. This method is automatically and transparently invoked to produce a steady continuum dataflow. To test our proposal, we conducted a case study about the processing of medical and satellite data. The evaluation revealed that this method creates continuum dataflows, without neither characterising workloads nor knowing infrastructure details, which yields a competitive performance with state-of-the-art solutions.
Fine-grained accelerator partitioning for Machine Learning and Scientific Computing in Function as a Service Platform

Function-as-a-service (FaaS) is a promising execution environment for high-performance computing (HPC) and machine learning (ML) applications as it offers developers a simple way to write and deploy programs. Nowadays, GPUs and other accelerators are indispensable for HPC and ML workloads. These accelerators are expensive to acquire and operate; consequently, multiplexing them can increase their financial profitability. However, we have observed that state-of-the-art FaaS frameworks usually treat accelerator as a single device to run single workload and have little support for multiplexing accelerators.
Reinforcement learning based CPG-controlled method with high adaptability and robustness: An experimental study on a robotic fishtail

An adaptive and robust control is imperative for robots operating in complex environments. The artificial intelligence approach presents a promising solution, while training and practical application in an actual robot remain a significant challenge. This paper proposes a reinforcement learning based control strategy with lightweight computation, aiming to optimize thrust in complex flows and varying self-structural characteristics of robotic fishtails. Improved Q-learning algorithm and CPG control are utilized to enable the robotic fishtail to make autonomous and accurate decisions in response to unknown changes. The control strategy is trained in actual physical flow fields, with an action selection strategy and a reward system proposed to accelerate convergence and enhance training stability. An integrated system of online learning, response measuring, and real-time monitoring is developed for the training and testing processes. Variable environmental tests are performed under different turbulent flows. Distinct caudal fins are utilized to conduct tests on self-structural variation. The experimental results confirm the robustness and adaptability of the control strategy, as well as its superiority over the PID approach in terms of accuracy, stability, and response speed. The control strategy architecture and physical experimental method could offer experience and application reference for the intelligent control of actual robots.
Knitting Skill Training for PKK Members in Sungai Pauh Pusaka Village Langsa City

Purpose: This community service aimed to address challenges PKK members face in Sungai Pauh Pusaka Village, including a need for knitting skill training and knowledge. The goal was to empower these members with practical skills to create knitting products, enhancing their self-sufficiency and village development.&#x0D; Method: The project involved stages like preparation, implementation, and evaluation. Coordination with the PKK head set the schedule. Training included tools and material introduction. Satisfaction surveys gauged participant motivation.&#x0D; Practical Applications: The practical applications of this community service initiative are noteworthy. It has empowered PKK members with valuable knitting skills, making them more self-sufficient and capable of creating knitting products. These newfound skills can be applied in various real-world scenarios, potentially leading to economic opportunities for the participants and contributing to the development of Sungai Pauh Pusaka Village.&#x0D; Conclusion: The initiative achieved an 80.0% satisfaction rate, highlighting the success of University Samudra's knitting skill training in empowering PKK members and benefiting the community.