Reviewerly/exHarmony huggingface dataset:
USING THE ARDUINO PLATFORM IN THE TRAINING OF COMPUTER SCIENCE TEACHERS ON THE PRINCIPLES OF STEM LEARNING

Modern education is continuously evolving, primarily focused on innovative teaching methods. Among the innovative and promising areas of computer science teacher training, in line with STEM education principles, is the incorporation of the Arduino platform into the educational process. Arduino provides students with an avenue to grasp programming and digital technologies in detail. This fosters critical and creative thinking, essential attributes in the education of future teachers. The integration of information and communication technologies into the educational system revamps pedagogies by broadening horizons and elevating the effectiveness of educational activities. Arduino's practical orientation empowers future educators to transform abstract concepts into tangible projects, promoting active and engaging learning opportunities for students. Utilizing Arduino can help educators foster students' interest and motivation by facilitating their development of critical skills necessary for the science and technology fields. As STEM education becomes increasingly vital in modern education, the integration of science, technology, engineering, and mathematics has become a central focus. The integration of Arduino in education facilitates the realization of STEM education principles by merging theoretical knowledge with practical skills and cultivating students' creative abilities. The integration of Arduino in the instructional training of IT professionals advances the development of modern industry-compliant competencies. This approach not only equips students to work in the IT industry, but also integrates them into the realm of STEM education, empowering them to uncover their potential and hone their practical skills. The study's findings verify that incorporating Arduino into the training process of computer science educators holds promise, empowering the emergence of talented and proficient teachers capable of efficiently implementing STEM methodologies in future educational undertakings. This integration cultivates a distinctive milieu that fosters the cultivation of critical thinking, creativity, and practical skills in students, while equipping them with the necessary competencies to confront the challenges of the contemporary information age.
PRINCIPLES OF MILITARY MEDICAL TRAINING IN THE CONTEXT OF DIGITAL TRANSFORMATION IN HEALTHCARE

The work is devoted to the problem of military medical training in the context of digital transformation in healthcare. The aim of this work is to justify and highlight the directions for implementing specific principles of military medical training in the context of digital transformation in healthcare. The expediency of specific principles in military medical training (the principle of innovation, the principle of orientation to network services, and the principle of reliance on digital tools) is substantiated; the content and essence of specific principles have been clarified; directions for the implementation of specific principles in the process of military medical training are determined. It is shown that the need for an innovative focus in military medical training is driven by several factors, including organizational innovations that contribute to the effective transformation of the healthcare system, digital innovations aimed at automating processes related to medical data, innovative medical technologies, and pharmaceutical and medical-technical innovations associated with the use of advanced methods for diagnosis, treatment, and patient care. It is established that the principle of orientation towards network services allows for effectively combining formal, informal, and informal education for military doctors, fostering lifelong self-education skills. We specify that the principle of orientation towards network services involves familiarizing military doctors with the results of fundamental research in the medical field and relies on the open access to full-text digital publications, both domestic and international. The principle of reliance on digital tools recognizes the potential of digital tools and the appropriateness of their use in the process of military medical training.
Density Peak Clustering Load Curve Based on Mutual Local Density and Multi Cluster Merging

It is indispensable and important work in the context of electric power big data to extract and cluster analysis of electricity load users accurately, rapidly and efficiently, so as to obtain the user’s electricity consumption behavior and demand pattern. To address the problem that density peak clustering algorithms have difficulty in identifying the cluster centers of low-density datasets, which results in poor clustering results, and the problem that single-step assignment strategy is highly likely to lead to sample misallocation, this paper proposes a density peak load curve clustering algorithm based on mutual local density and multi-cluster merging. First, a new local density is defined based on the K-nearest neighbor algorithm. Next, the density peaks of all sample points are calculated, and the first m samples are selected as the initial density peaks. Then, the clusters are divided into micro-clusters according to the Pearson correlation between the sample points and the initial density peaks. Finally, the global and local features of each class cluster are considered and new fusion criteria are defined to achieve the fusion of micro-clusters. The algorithm analysis shows that the proposed algorithm can effectively identify the class cluster centers of low-density clusters. Moreover, compared with the single-step assignment strategy, the proposed multi-cluster fusion iterative strategy in this paper is superior in terms of cluster evaluation metrics.
Machine Learning on Dynamic Graphs: A Survey on Applications

Dynamic graph learning has gained significant attention as it offers a powerful means to model intricate interactions among entities across various real-world and scientific domains. Notably, graphs serve as effective representations for diverse networks such as transportation, brain, social, and internet networks. Furthermore, the rapid advancements in machine learning have expanded the scope of dynamic graph applications beyond the aforementioned domains. In this paper, we present a review of lesser-explored applications of dynamic graph learning. This study revealed the potential of machine learning on dynamic graphs in addressing challenges across diverse domains, including those with limited levels of association with the field.
SMG: A System-Level Modality Gating Facility for Fast and Energy-Efficient Multimodal Computing

Achieving low-latency and high-efficiency multimodal computing (MMC) is crucial for deploying high-performance autonomous embedded systems (AES) that has limited energy budgets. However, existing methods have mainly focused on optimizing the computing phase and have overlooked the significant energy and latency overhead during the sensing phase. Therefore, we propose SMG, a system-level modality gating facility to optimize this. Our approach introduces a software-defined DSP gating technique that enables MMC tasks to bypass both the sensing and computing phases of unimportant modalities. We also propose a raw data-activated MMC mechanism that comprises a fast modality tester and adaptive modality executor, which adapts to the modality gating architecture and performs energy-efficient MMC. To evaluate SMG, we implement a prototype of SMG by integrating it into existing AES and analyze it with extensive multimodal video recognition workloads. Our experimental results show that SMG outperforms SOTA approaches by adaptively gating some DSP operations, resulting in substantial improvements in both energy consumption and task latency.
Progressive Neural Compression for Adaptive Image Offloading Under Timing Constraints

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers.Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable.Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints.It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices.In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem.Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timingconstrained offloading over variable bandwidth.To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance.Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline.We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.
RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics

Intelligent robots are designed to effectively navigate dynamic and unpredictable environments laden with moving mechanical elements and objects. Such environment-induced dynamics, including moving obstacles, can readily alter the computational demand (e.g., the creation of new tasks) and the structure of workloads (e.g., precedence constraints among tasks) during runtime, thereby adversely affecting overall system performance. This challenge is amplified when multi-task inference is expected on robots operating under stringent resource and real-time constraints. To address such a challenge, we introduce RED, a systematic real-time scheduling approach designed to support multi-task deep neural network workloads in resource-limited robotic systems. It is designed to adaptively manage the Robotic Environmental Dynamics (RED) while adhering to real-time constraints. At the core of RED lies a deadline-based scheduler that employs an intermediate deadline assignment policy, effectively managing to change workloads and asynchronous inference prompted by complex, unpredictable environments. This scheduling framework also facilitates the flexible deployment of MIMONet (multi-input multi-output neural networks), which are commonly utilized in multi-tasking robotic systems to circumvent memory bottlenecks. Building on this scheduling framework, RED recognizes and leverages a unique characteristic of MIMONet: its weight-shared architecture. To further accommodate and exploit this feature, RED devises a novel and effective workload refinement and reconstruction process. This process ensures the scheduling framework's compatibility with MIMONet and maximizes efficiency. We have implemented RED on several widely used embedded and mobile platforms, including the NVIDIA Jetson Nano, TX2, Xavier, and Orin platforms. We evaluated its performance using workloads that span a broad range of settings typical in navigation robots. The experimental results demonstrate that RED surpasses existing approaches (often by a significant margin) across critical metrics such as throughput, timing correctness, interference robustness, adaptability, and overhead.
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models

Recent advancements in language models (LMs) have gained substantial attentions on their capability to generate human-like responses. Though exhibiting a promising future for various applications such as conversation AI, these LMs face deployment challenges on various devices due to their extreme computational cost and unpredictable inference latency. Such varied inference latency, identified as a consequence of uncertainty intrinsic to the nature of language, can lead to computational inefficiency and degrade the overall performance of LMs, especially under high-traffic workloads. Unfortunately, the bandwidth of these uncertainty sources is extensive, complicating the prediction of latency and the effects emanating from such uncertainties. To understand and mitigate the impact of uncertainty on real-time response-demanding systems, we take the first step to comprehend, quantify and optimize these uncertainty-induced latency performance variations in LMs. Specifically, we present RT-LM, an uncertainty-aware resource management ecosystem for real-time inference of LMs. RT-LM innovatively quantifies how specific input uncertainties, recognized within the NLP community, adversely affect latency, often leading to an increased output length. Exploiting these insights, we devise a lightweight yet effective method to dynamically correlate input text uncertainties with output length at runtime. Utilizing this quantification as a latency heuristic, we integrate the uncertainty information into a system-level scheduler which explores several uncertainty-induced optimization opportunities, including uncertainty-aware prioritization, dynamic consolidation, and strategic CPU offloading. Quantitative experiments across five state-of-the-art LMs on two hardware platforms demonstrates that RT-LM can significantly reduce the average response time and improve throughput while incurring a rather small runtime overhead.
RT-Blockchain: Achieving Time-Predictable Transactions

Although blockchain technology is being increasingly utilized across various fields, the challenge of providing timing guarantees for transactions remains unmet, which is an obstacle in implementing blockchain solutions for time-sensitive applications such as high-frequency trading and real-time payments. In this paper, we propose the first solution to achieve a timing guarantee on blockchain. To this end, we raise and address two issues for timely transactions on a blockchain: (a) architectural support, and (b) real-time scheduling principles specialized for blockchain. For (a), we modify an existing blockchain network, offering an interface to preferentially select the transactions with the earliest deadlines. We then extend the blockchain network to provide the flexibility of the number of generated blocks at a single block time. Under such architectural supports, we achieve (b) with three steps. First, to resolve a discrepancy between a periodic request of a transaction-generating node and the corresponding arrival on a block-generating node, we translate the former into the latter, which eases the modeling of the transaction load imposed on the blockchain network. Second, we derive a schedulability condition of the modeled transaction load, which guarantees no missed deadline for all transactions under a work-conserving deadline-based scheduling policy. Last, we develop a lazy scheduling policy and its condition, which reduces the number of generated blocks without compromising the degree of timing guarantees for the work-conserving policy. By implementing RT-blockchain on top of an existing open-source blockchain project, we demonstrate the effectiveness of the proposed scheduling principles with architectural supports in not only ensuring timely transactions but also reducing the number of generating blocks.
$\mathrm{R}^{3}$: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics

Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous adaptation of Deep Reinforcement Learning (DRL) models in dynamic environments. This research is fundamentally motivated by the need to understand and address the challenges of on-device real-time DRL, which involves balancing timing and algorithm performance under memory constraints, as exposed through our extensive empirical studies. This intricate balance requires co-optimizing two pivotal parameters of DRL training - batch size and replay buffer size. Configuring these parameters significantly affects timing and algorithm performance, while both (unfortunately) require substantial memory allocation to achieve near-optimal performance. This paper presents <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> , a holistic solution for managing timing, memory, and algorithm performance in on-device real-time DRL training. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> employs (i) a deadline-driven feedback loop with dynamic batch sizing for optimizing timing, (ii) efficient memory management to reduce memory footprint and allow larger replay buffer sizes, and (iii) a runtime coordinator guided by heuristic analysis and a runtime profiler for dynamically adjusting memory resource reservations. These components collaboratively tackle the trade-offs in on-device DRL training, improving timing and algorithm performance while minimizing the risk of out-of-memory (OOM) errors. We implemented and evaluated <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> extensively across various DRL frameworks and benchmarks on three hardware platforms commonly adopted by autonomous robotic systems. Additionally, we integrate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> with a popular realistic autonomous car simulator to demonstrate its real-world applicability. Evaluation results show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> achieves efficacy across diverse platforms, ensuring consistent latency performance and timing predictability with minimal overhead. Moreover, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> showcases versatility by handling varied optimization goals and adapting to fluctuating systems scenarios.
Brief Industry Paper: Evaluating Robustness of Deep Learning-Based Recommendation Systems Against Hardware Errors: A Case Study

Deep learning-based recommendation systems (DL-RMs) are industry-scale recommendation models developed by Meta, designed to make use of both categorical and numerical inputs to make personalized recommendations. To serve billions of users in real-time, DLRMs rely on high-performance hardware and accelerators within our data centers, optimizing for execution latency and recommendation quality. However, continuous technology scaling, expanding workload, and increasing hardware heterogeneity could lead to increased risk of hardware errors. Addressing this risk often involves introducing extra design redundancy, which can pose a non-negligible overhead in performance and latency. In this paper, we present a case study of evaluating DLRM robustness against hardware errors by performing an extensive error injection campaign to DLRM. Our findings unveil that DLRM is notably robust to hardware errors and we further find that embedding tables in DLRM show an especially strong robustness. Additionally, we explore a software-level error mitigation techniques, activation clipping, for mitigating the hardware errors, which improves the DLRM robustness further. This industrial case study of understanding and improving DLRM robustness can enable the system to continue to deliver timely recommendations even in the presence of hardware challenges, or reduce the timing latency overhead posed by design redundancy, enhancing overall recommendation system performance.
Human-Centered Evaluation of XAI Methods

In the ever-evolving field of Artificial Intelligence, a critical challenge has been to decipher the decision-making processes within the so-called "black boxes" in deep learning.Over recent years, a plethora of methods have emerged, dedicated to explaining decisions across diverse tasks.Particularly in tasks like image classification, these methods typically identify and emphasize the pivotal pixels that most influence a classifier's prediction.Interestingly, this approach mirrors human behavior: when asked to explain our rationale for classifying an image, we often point to the most salient features or aspects.Capitalizing on this parallel, our research embarked on a user-centric study.We sought to objectively measure the interpretability of three leading explanation methods: (1) Prototypical Part Network, (2) Occlusion, and (3) Layer-wise Relevance Propagation.Intriguingly, our results highlight that while the regions spotlighted by these methods can vary widely, they all offer humans a nearly equivalent depth of understanding.This enables users to discern and categorize images efficiently, reinforcing the value of these methods in enhancing AI transparency.
Self-supervision meets kernel graph neural models: From architecture to augmentations

Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the most prevailing algorithmic tool. Despite its popularity, the family of MPNNs suffers from several drawbacks such as transparency and expressivity. Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs). Developments on KGNNs are currently a nascent field of research, leaving several challenges from algorithmic design and adaptation to other learning paradigms such as self-supervised learning. In this paper, we improve the design and learning of KGNNs. Firstly, we extend the algorithmic formulation of KGNNs by allowing a more flexible graph-level similarity definition that encompasses former proposals like random walk graph kernel, as well as providing a smoother optimization objective that alleviates the need of introducing combinatorial learning procedures. Secondly, we enhance KGNNs through the lens of self-supervision via developing a novel structure-preserving graph data augmentation method called latent graph augmentation (LGA). Finally, we perform extensive empirical evaluations to demonstrate the efficacy of our proposed mechanisms. Experimental results over benchmark datasets suggest that our proposed model achieves competitive performance that is comparable to or sometimes outperforming state-of-the-art graph representation learning frameworks with or without self-supervision on graph classification tasks. Comparisons against other previously established graph data augmentation methods verify that the proposed LGA augmentation scheme captures better semantics of graph-level invariance.