Reviewerly/exHarmony huggingface dataset:
Transformation of Research and Innovations in the Sphere of Teacher Education: Reviewing the Results of the 9th International Forum on Teacher Education (IFTE-2023)

The aim of the study was to organize a reflexive understanding of the issues and trends in the development of the research in teacher education in Russia through the analysis of some aspects of the International Forum on Teacher Education on the basis of Kazan Federal University (https://ifte.kpfu.ru/). In this context, three tasks were solved: the development of the concepts of the "field description" of conference activities; formation of a preliminary "field image" of the IFTE-2023, identification of leading trends and urgent tasks in the organization of conference events in the near future. The research methodology included three components: conducting a survey of the Forum participants, aimed at identifying existing priorities, attitudes in the field of teacher education; analysis of the topics of "key reports"; analysis of the statements of experts participating in the panel discussion on the problems of teacher education, held on May 25, 2023 as part of IFTE-2023. The results obtained allow us to draw conclusions in the following aspects: on the use of key ideas for the study of research and innovation activities in the field of education; generalized requirements for the design of conference-oriented events on teacher education; preliminary assessment of the relevance and significance of events; conditions for improving the quality and efficiency of conference discussions; priority areas of research on practical pedagogy; activation of public participation in interdisciplinary and inter institutional projects; priorities for the transformation of teacher education.
BUILDING A REAL-TIME INFORMATION AND FLASH FLOODS, LANDSLIDES EARLY WARNING SYSTEM IN MOUNTAINOUS AND MIDLAND AREAS OF VIET NAM

Flash flood is a natural disaster occurring shortly on small scale albeit with great devastation. In fact, the current flash flood events, landslide investigations and surveys from research agencies and local authorities play an important role in creating diverse data sources for researching and exploring the signs as well as modeling adjustment for detecting the risks. This article presents the results of making a Web portal used for the reception and integration of early warnings from citizens, local authorities and governing bodies against flash floods and landslides. This system collects real time rainfall data from the CDH of Meteorology and Hydrology Administration, integrates the numerical weather prediction WRF (the Weather Research and Forescast) model with 6 h, 24 h, 48 h forecast lead time and SEAFFGS product results. Also, it would elaborate on the system providing tools for aiding, analysing, producing and disseminating warning bulletins against flash floods and landslides for mountainous and midland areas, sending warning message about high risk flash flood and landslide with the total observed rainfall or rainfall forecast over the threshold via email or Zalo automatically. This information which can be accessed everywhere via the internet, will effectively support the forecasters in mornitoring and publishing the flash flood and landslide warning bulletins; help the authorities and citizens become aware of natural disasters early and mitigate damage caused by flash floods and landslides.
Blind Approach Using Convolutional Neural Networks to a New Ultrasound Image Denoising Task

Speckle noise reduction from ultrasound images still represents a challenge. One limiting factor is the difficulty in noise characterization and modeling. Specifically, the removal of this image-correlated noise is often obtained at the expense of signal degradation. Convolutional neural networks have been shown to reach good performance along with generalizability properties. In this work, we tested an unsupervised neural network, Noise2Noise, to reduce speckle noise from ultrasound images. The network training used only noisy images obtained adding synthetic noise to real ultrasound images. The trained networks have been tested both on synthetic noisy images and on real ultrasound acquisitions, showing promising results that speak in favor of this novel denoising approach.
Handling Noisy Data in Eye-Tracking Research: Methods and Best Practices

Eye-tracking research plays a crucial role in understanding human behavior, cognitive processes, and user interactions. However, the accuracy and reliability of the findings can be compromised due to the presence of noise and outliers in eye-tracking data. In this study, we delve into various methodologies, such as filtering, outlier identification, and data preparation, to address these challenges. We aim to comprehensively review and analyze different approaches for handling noisy eye-tracking data, including filtering methods, outlier identification algorithms, and data pretreatment strategies. By comparing these methodologies, we aim to shed light on their respective advantages, disadvantages, and trade-offs. Our research will provide researchers with a comprehensive understanding of the available options, empowering them to make informed decisions based on their specific research objectives. Moreover, we will offer valuable guidelines and best practices for effectively processing noisy eye-tracking data. Through this endeavor, we strive to enhance the robustness and reliability of eye-tracking research, thereby advancing our understanding of human behavior and cognitive processes.
RANSAC as a Method for Reliable Line Identification for Efficient Estimation of the Instantaneous Frequency Rate

The random sample consensus (RANSAC) is applied as an effective approach for parameter estimation in case of presence of outliers. The CPF-RANSAC estimator is proposed and evaluated on signals embedded in high noise. Cubic phase function (CPF) is the distribution used for an efficient estimation of instantaneous frequency rate (IFR) for non-stationary signals with polynomial phase of the third order, called cubic phase (CP) signals. The most popular polynomial phase signals (PPS) are signals with the polynomial phase of the second order i.e., signals with linear frequency modulation (LFM). In real environment LFM signals are frequently distorted by additional third order component, what results in CP signals. Such signals appear as a line on the time-instantaneous frequency rate (T-IFR) plane, the parameters of which depend on the phase parameters of the signal. Thus, the problem under consideration concerns line estimation in a T-IFR plane. The IFR estimates are obtained by searching maximum of the CPF representation. Then the CPF-RANSAC strategy randomly selects a subset of data samples obtained from the CPF distribution of signals and uses them to estimate parameters of line. Optimal fitting to the line is obtained by selection of points based on the maximum likelihood (ML) criterion.
Voda: A GPU Scheduling Platform for Elastic Deep Learning in Kubernetes Clusters

Nowadays, machine learning has become an indispensable service for cloud providers. Elastic training, a novel training paradigm that dynamically adjusts resource allocation for a group of training jobs, has gained popularity due to its ability to effectively utilize accelerators, which are essential for training a massive number of deep learning models. Despite the existence of numerous scheduling algorithms for elastic training, most lack an easy-to-use yet efficient platform to execute them. In this paper, we present Voda, a GPU scheduling platform for elastic deep learning. In contrast to prior approaches that employ parameter servers for elastic training, Voda is designed for AllReduce-style communication, which proves to be more effective, albeit more complex to adjust. Voda, built on top of Kubernetes, consists of a set of loosely coupled components that collect runtime information, dynamically alter the resource allocation, and optimize job placement based on communication costs among underlying GPUs. We implement and compare four scheduling algorithms for elastic training, including three existing methods and one newly proposed, on Voda, with different workloads, job distributions, and arrival patterns. Experimental results demonstrate that no single algorithm dominates all performance metrics, such as average job completion time, running time, or makespan. However, certain algorithms outperform others under specific workloads and job distributions. Additionally, our experiments highlight the significance of job placement in GPU clusters, and our proposed method effectively optimizes communication costs among different workers of a job.
REFORM: Increase alerts value using data driven approach

We introduce REFORM as a data-driven approach to assess alert quality and increase the value of alerts, reduce alert noise and to identify gaps in alert coverage. In the context of monitoring cloud infrastructure and services, surfacing the right set of alerts to human operators is critical to ensure timely intervention for issues that may otherwise result in significant impact to customers as well as for avoiding operator burnout.To assess alert quality we focus on the notion of actionable alerts – a specific alert is categorized as actionable or not based on historic evidence of operator actions having been taken in response to such alerts. Using alert data collected over a six month period from a combination of cloud environments that include pre-production and production systems, alerts are categorized as true positive, false positive or false negative with respect to their actionability. Based on this, we then introduce and quantify precision and miss rates per alert category (trigger condition) from the historic data. Noisy, non-actionable alerts are identified using precision. Gaps in coverage are identified using miss rate, or failure of monitoring to detect issues.A prescriptive approach is then provided to increase the alerts’ value that includes refinement of alert definitions, replacement of alerts that are no longer actionable and identification of alerting coverage gaps. We monitored our REFORM system over three months and found that the volume of noisy, non-actionable alerts were significantly reduced and furthermore, this resulted in significantly higher intervention rates from the operators.
Communication Channel-Aware Federated Distillation For Massive MIMO Systems

Federated Distillation (FD) is a novel Federated Learning (FL) paradigm which not only shares the privacy-preserving nature of FL but also provides possible solutions to the challenges introduced by FL framework. In this paper, a communication channel-aware FD framework is investigated under a massive MIMO communication system. We manage to reduce the overall communication overhead in each round and exclusively adopt logits as the information exchanged in the wireless links. Several system schemes have been designed and proposed with the aim of thrifting communication resources and accelerating training process. Simulation results on image classification with non-i.i.d. CIFAR-10/STL-10 datasets have demonstrated both the effectiveness and efficiency of the proposed framework.
Multiple Scanning Order in Fast Depth Estimation Algorithm

Fast algorithms for stereoscopic depth estimation typically employ processing of the input images in a Single Scanning Order (SSO). In this paper we present a novel approach that employs processing in Multiple Scanning Orders (MSO), which are then merged together into a final depth map. We demonstrate the advantages of the proposal on the example of a fast depth estimation technique [1], adaptable both for mobile platforms and FPGA. We show that application of the proposal leads to considerable quality improvement at an acceptable complexity cost.
Few-Shot Modulation Recognition with Feature Transformation and Meta-Learning

Few-shot learning (FSL) has attracted much attention in the fields of image and audio classification, but few efforts have been made on few-shot modulation recognition. It requires classification models to quickly adapt to new modulation recognition tasks with a shift in task distribution. The task distribution shift between training and testing tasks is critical for FSL-based automatic modulation recognition (AMR). In this paper, we propose a few-shot modulation recognition framework using model-agnostic meta-learning (MAML). Our framework includes a single vector in the meta-training structure to mitigate the permutation problem of class label assignments during meta-testing and a feature transformation function in the meta-testing structure to alleviate the channel bias caused by the task distribution shift. We provide an in-depth analysis about the effectiveness of the proposed AMR framework on few-shot modulation recognition. Experimental results demonstrate the proposed framework can greatly improve the generalization ability of learned features and thus the modulation accuracy. Moreover, comparison experiments show that the proposed method outperforms four baseline methods in the few-shot setting.
Vela: A 3-Phase Distributed Scheduler for the Edge-Cloud Continuum

The amalgamation of multiple Edge and Cloud clusters into an Edge-Cloud continuum requires efficient scheduling techniques to cope with high numbers of infrastructure nodes and computing jobs. Since monolithic schedulers typically do not scale well beyond a certain cluster size, distributed scheduling approaches are usually employed to address such scalability issues. Distributed schedulers are often designed for Cloud environments and lack support for the Edge. Conversely, many Edge schedulers focus on single clusters and provide limited support to deal with the scale of the Edge-Cloud continuum. In this paper, we present the Vela Distributed Scheduler, a globally distributed scheduler, which is specifically tailored for the Edge-Cloud continuum. The main contributions of our work include: i) A novel, globally distributed and orchestrator-independent scheduler with a 3-phase scheduling workflow; ii) A two-level, informed sampling mechanism, which reduces latency for globally distributed sampling and leverages job requirements to produce high quality node samples; And iii) a MultiBind mechanism that significantly reduces job evictions and rescheduling due to scheduling conflicts. We implement Vela on top of Kubernetes and evaluate it in a realistic large-scale setup using multiple interconnected, globally distributed, and production-ready MicroK8s clusters with up to 20,000 total simulated nodes. Our results show that Vela’s performance scales linearly with infrastructure size and that it reduces scheduling conflicts by a factor of 10.
Multi-Objective Workflow Scheduling to Serverless Architecture in a Multi-Cloud Environment

Many complex workflows consist of multiple tasks represented as a directed acyclic graph (DAG). Optimal deployment of such workflows on a cloud using multiple services requires a judicious selection of compute and storage services to minimize the makespan and the cost of deployment. Multi-cloud deployment is emerging as a preferred choice for the deployment of complex workflows for price competitiveness and freedom from vendor lock-in. However, finding an optimal mapping scheme for heterogeneous tasks of a workflow in a multi-cloud environment is a challenge. Furthermore, each participating cloud service provider (CSP) has a unique cost model and maximum deliverable performance. This makes exploration of the optimal configuration of the chosen service daunting. Many algorithms, frameworks, and tools have been proposed to schedule complex workflows on cloud using virtual machines (VMs) available as Infrastructure-as-a-Service (IaaS). However, the use of scalable and cost-effective serverless platforms offered as Function-as-a-Service (FaaS) is still in its infancy.In this work, we use particle swarm optimization (PSO) for mapping complex workflows to cloud services such as computing, and storage in a multi-cloud scenario. We map complex workflows to the serverless platforms and storage services from popular cloud vendors namely Amazon Web Services (AWS), Azure (AZR), and Google Cloud Platform (GCP). The experimental evaluation shows that our approach results in up to 61% improvement in makespan and 51% improvement in the cost of workflow deployment as compared to naive and intuition-based mapping in a cloud.
Asynchronous Personalized Learning for Heterogeneous Wireless Networks

The future wireless networks are expected to support more artificial intelligence (AI)-enabled applications, such as Metaverse services, at the network edge. The AI algorithms, like deep learning, play an important role in extracting important information from a large dataset, but conventional centralized learning requires collecting the datasets that are distributed over the users and always include their personal information. Federated learning (FL) has been widely investigated to address those issues by performing learning in a distributed manner. However, it shows performance degradation for heterogeneous networks. In this paper, we introduce asynchronous and personalized FL to address the heterogeneity from different aspects. We first propose a semi-asynchronous FL (Semi-Async-FL) by adding time lag to distributed global model and enabling aggregation while receiving a small set of users. Specifically, we propose a new asynchronous-based personalized FL (Async-PFL) algorithm by considering the staleness of the personalized models in classic personalized FL. The simulations show that our proposed Async-PFL achieves better learning performance than Semi-Async-FL and personalized FL.
Radio Frequency Fingerprints Identification based on GAN Networks

In this work, we tackle challenges in Radio Frequency Fingerprint Identification (RFFI) related to insufficient training data and out-of-data (OOD) situations. The great success of generative-based models in deep learning, i.e., the great potential of generative adversarial networks (GANs) for deep learning-based data augmentation and OOD detection, prompted us to propose GAN-based approaches to improve the recognition accuracy of the supervised learning tasks in RFFI systems, as well as improving the success rate in anomalous data detection. Due to the temporal correlation of electromagnetic signals, we propose a one-dimensional convolution-based Auxiliary Classifier GAN (AC-GAN) network for data augmentation and a one-dimensional convolution-based RF-GANomaly network for anomalous data detection. The experimental results on real-world data show that the proposed GAN-based data augmentation method significantly improves recognition accuracy, and the proposed RF-GANomaly model achieves superior anomalous data detection performance compared to our baseline GAN-based anomalous data detection methods.
Enhancing CNN-based Channel Estimation using Transfer Learning in OFDM Systems

In this paper, we investigate the application of deep transfer learning to channel estimation for an orthogonal frequency division multiplexing (OFDM) system. Recently, deep learning has been applied for channel estimation and shown its promising performance, while it suffers in the presence of a mismatch between the training phase channel model and real-world channel conditions. In the following, we deploy two different convolutional neural network (CNN) models from the literature and highlight their performance degradation caused by mismatch problems of power delay profile (PDP) and Doppler spreads. Transfer learning then is deployed to resolve the mismatch problem without the need to completely retrain CNN models. Our results show that we can alleviate the degradation using smaller efforts with transfer learning, especially for CNN with a deeper structure. In the end, when comparing transfer learning and data augmentation, our study also indicates that transfer learning is the better choice when coping with channel mismatch problems.
A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes

As Exascale computing becomes a reality, the energy needs of compute nodes in cloud data centers will continue to grow. A common approach to reducing this energy demand is to limit the power consumption of hardware components when workloads are experiencing bottlenecks elsewhere in the system. However, designing a resource controller capable of detecting and limiting power consumption on-the-fly is a complex issue and can also adversely impact application performance. In this paper, we explore the use of Reinforcement Learning (RL) to design a power capping policy on cloud compute nodes using observations on current power consumption and instantaneous application performance (heartbeats). By leveraging the Argo Node Resource Management (NRM) software stack in conjunction with the Intel Running Average Power Limit (RAPL) hardware control mechanism, we design an agent to control the maximum supplied power to processors without compromising on application performance. Employing a Proximal Policy Optimization (PPO) agent to learn an optimal policy on a mathematical model of the compute nodes, we demonstrate and evaluate using the STREAM benchmark how a trained agent running on actual hardware can take actions by balancing power consumption and application performance.
Towards a Holistic Cloud System with End-to-End Performance Guarantees

Computing technologies are undergoing a relentless evolution from both the hardware and software sides, incorporating new mechanisms for low-latency networking, virtualization, operating systems, hardware acceleration, smart services orchestration, serverless computing, hybrid private-public Cloud solutions and others. Therefore, Cloud infrastructures are becoming increasingly attractive for deploying a wider and wider range of applications, including those with more and more stringent timing constraints, like the emerging use case of deploying time-critical applications. However, despite the availability of a number of public Cloud offerings, and of products (or open-source suites) for deploying in-house private Cloud infrastructures, still there are no solutions readily available for managing time-critical software components with predictable end-to-end timing requirements in the range of hundreds or even tens of milliseconds. The goal of this discussion is to present the multi-domain challenges associated with orchestrating a holistic Cloud system with end-to-end guarantees, which is the subject of my current PhD investigations.
A Hybrid Wireless Image Transmission Scheme with Diffusion

We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves.