Reviewerly/exHarmony huggingface dataset:
Augmenting ML-based Predictive Modelling with NLP to Forecast a Job's Power Consumption

As modern High-Performance Computing (HPC) reach exascale performance, their power consumption becomes a serious threat to environmental and energy sustainability. Efficient power management in HPC systems is crucial for optimizing workload management, reducing operational costs, and promoting environmental sustainability. Accurate prediction of job power consumption plays an important role in achieving such goals. In this paper, we apply a technique combining Machine Learning (ML) algorithms with Natural Language Processing (NLP) tools to predict job power consumption. The solution is able to predict job maximum and average power consumption per node, leveraging only information which is available at the time of job submission. The prediction is performed in an online fashion, and we validate the approach using batch system logs extracted from Supercomputer Fugaku, hosted at the RIKEN Center for Computational Science, in Japan. The experimental evaluation shows promising results of outperforming classical technique while obtaining an R2 score of more than 0.53 for our two prediction tasks.
CARAT KOP: Towards Protecting the Core HPC Kernel from Linux Kernel Modules

Extending Linux through the kernel module interface can offer immense benefits and capabilities in high performance computing (HPC). These extensions can also be more readily deployed because Linux is the common, typically only, supported OS choice among supercomputing vendors. However, because Linux is monolithic, Linux kernel modules are free to read and write any address with kernel-level permissions. A poorly written—or untrustworthy—module can wreak havoc on the whole system. This unfortunately means that many production HPC systems often do not permit custom kernel modules to be inserted into the system, no matter the benefit.
VSCuda: LLM based CUDA extension for Visual Studio Code

The CUDA programming language has developed to constantly accommodate new functionalities since its introduction. For beginners in particular, it is already difficult to remember the expected function parameters of commonly used CUDA features, let alone optimize their code by exploiting newly introduced functionalities. To reduce the burden for CUDA programmers, we propose VSCuda, a Visual Studio Code extension for CUDA C/C++ that includes functionalities including but not limited to CUDA syntax highlighting, code help for all CUDA APIs, code completion for all CUDA functions, and integrated code improvement suggestions from state-of-the-art large language models.
Understanding Energy Performance of Containers Deployment on HPC-Based post-Moore Platforms

HPC platforms seek to ensure peak computing performance with minimal energy cost searching sustainability. Considering different cases of use and implementation, both post-Moore hardware elements and software deployment (as virtualization or containerization) are incorporated. However, as the number of devices proliferates, managing applications has become intricate, prompting the adoption of containerization methods for simplification. Then, understanding the performance of the deployment strategy to propose an adequate implementation and integration of HPC-based post-Moore architectures is important to guarantee efficiency. This study evaluates the containerization strategies on a cost-effective post-Moore device suitable for an HPC platform. Factors like ease of use, reproducibility, and compatibility examine the deployment methods. The evaluation process employs metrics establishment and stress tests to appraise application-specific aspects. The obtained insights are categorized to address deployment mechanisms, performance implications, execution duration, and energy consumption impacts.
Addressing Stale Gradients in Scalable Federated Deep Reinforcement Learning

Advancements in reinforcement learning (RL) via deep neural networks have enabled their application to a variety of real-world problems. However, these applications often suffer from long training times. While attempts to distribute training have been successful in controlled scenarios, they face challenges in heterogeneous-capacity, unstable, and privacy critical environments ([3], [9]). This work applies concepts from federated learning (FL) to distributed RL, specifically addressing the stale gradient problem. A deterministic framework for asynchronous federated RL is utilized to explore dynamic methods for handling stale gradient updates in the Arcade Learning Environment. Experimental results from applying these methods to two Atari-2600 games demonstrate a relative speedup of up to 95% compared to plain A3C in large and unstable federations.
An Efficient Distributed Graph Engine for Deep Learning on Graphs

Traditional graph-processing algorithms have been widely used in Graph Neural Networks (GNNs). This combination has shown state-of-the-art performance in many real-world network mining tasks. Current approaches to graph processing in deep learning face two main problems. On the one hand, easy-to-use deep learning libraries lack support for widely used graph-processing algorithms and do not provide low-level APIs for building distributed graph-processing algorithms. On the other hand, existing graph-processing libraries are not user-friendly for deep learning researchers. Their graph primitives are not designed for batch processing, which is essential for deep learning use cases. In this paper, we present an efficient and easy-to-use graph engine that incorporates distributed graph processing into deep learning ecosystems. We develop a distributed graph storage system with an efficient batching technique to minimize communication overhead incurred by Remote Procedure Calls (RPC) between computing nodes. We propose an optimized method for distributed computation of Single Source Personalized PageRank (SSPPR) using the Forward Push algorithm based on lock-free parallel maps. Experimental evaluations demonstrate significant improvement, with up to three orders of magnitude in SSPPR throughput, of our graph engine compared with the tensor-based implementation. Both methods offer necessary usability with tensor operations, which are widely used for graph processing in current deep graph libraries.
Improvement of School Librarians' Abilities in Library Management with SLIMS Automation Software

Purpose: This research paper aims to highlight the importance of enhancing the skills and abilities of librarians in managing information within the context of school libraries. The study specifically focuses on the use of SLIMS automation software for school library management, addressing the significance of this endeavor in the field of information technology.&#x0D; Method: A workshop conducted on September 18, 2021, provided hands-on training for librarians in SLIMS software, covering installation, circulation, and reporting. Post-workshop support was provided through a WhatsApp group.&#x0D; Practical Applications: This research benefits both librarians, who gain vital IT skills, and the education sector, where efficient library management leads to improved information access and operations.&#x0D; Conclusion: By improving librarian skills with SLIMS software, this study contributes to more effective school library management, benefiting students and educators alike.
SoliTester: Detecting exploitable external‐risky vulnerability in smart contracts using contract account triggering method

Abstract The vulnerability in smart contracts (SCs) on the blockchain system may lead to severe security compromises. The SC can be invoked from an externally owned account (EOA) or a contract account (CA). The account a user creates to receive or send ether is an EOA. A CA contains codes that can interact with SCs. In Solidity SC, some vulnerabilities can only be exploited by the interactions between CAs and vulnerable SCs, which can be named external‐risky vulnerabilities . Most state‐of‐the‐art (SOTA) detectors detect external‐risky vulnerabilities by executing contract codes as an EOA user, thus reporting many unexploitable vulnerabilities. Therefore, we propose a CA‐triggering method to identify exploitable external‐risky vulnerabilities in Solidity SCs. We first designed agent contracts to simulate CAs' interactions with the target SCs in the real blockchain environment. We then detect vulnerability exploitation by analyzing transaction logs between agent contracts and target SCs and identifying successful exploits. We implemented the CA‐triggering method in a tool named SoliTester and evaluated it using three benchmark datasets, which contain three types of external‐risky vulnerabilities, namely, Reentancy (RE), Unchecked Call (UcC), and TxOrigin (TO). The results show that SoliTester can efficiently detect exploitable external‐risky vulnerabilities with significantly better precisions and recalls than SOTA detectors.
Block by block: a bibliometric analysis of blockchain in real estate

Blockchain technology is a novel and disruptive innovation that has the potential to transform various industries, such as finance, supply chain, and healthcare. However, the application and impact of blockchain technology in real estate remain largely unexplored. This study aims to investigate the characteristics, development, and structure of the research field of blockchain in real estate using bibliometric analysis. The study analyzes 352 publications indexed by the Web of Science database from 2016 to 2022 using different methods and tools, such as descriptive statistics, network analysis, co-citation analysis, and co-word analysis. The results show that blockchain in real estate is an interdisciplinary and rapidly growing research area that covers various topics and applications. The study also identifies the most influential publications, authors, journals, and countries in this field, as well as the main research themes and gaps. The study discusses the implications and contributions of the bibliometric analysis of the blockchain in real estate research and practice and suggests directions for future research on this topic.
Knowledge Management in the Metaverse

The Metaverse is becoming a hot topic of discussion and speculation. Its application in many fields is likely to affect the way we interact, socialize, get entertained, work, learn, and even conduct research. The Metaverse offers shared immersive 3D virtual spaces that are based on the integration of various technologies, including virtual reality and augmented reality. After an introduction to the Metaverse, this chapter presents how the Metaverse could help support knowledge management (KM) activities and practices in various aspects. An adaptation of the popular and well-accepted Knowledge Management SECI model from Nonaka and Takeuchi is used as a support to investigate how the Metaverse could help support the flow of knowledge in an organization. After reading this chapter, the reader will better understand all the potentials that the Metaverse can bring to support KM if the Metaverse ever takes off and keeps its promises!
Learning from the Future: Knowledge Management Systems in the Twenty-First Century

Despite technologies having an important role in supporting the management of knowledge, progress in (technical) knowledge management systems (KMS) in the past three decades has been uneven, incremental, and discrete. In the twenty-first century’s age of digitalization, comprehensive management of knowledge occurs in a far more complex environment, and overall speaking, current KMS are grossly inadequate in supporting knowledge workers and organizations in performing proactive and comprehensive knowledge-intensive activities and making decisions. A short summary and review of the evolution of KMS in the past three decades is outlined. Based on the rapidly advancing knowledge and technology landscape in the digital era, together with the gap between current and foreshadowed KM practices, the characteristics of both near- and long-term future KM systems, services, and delivery platforms are discussed. Such platforms are needed to support knowledge harnessing, learning, and innovation in a highly personalized and digitalized world. Particular emphasis is on reimagining the role of search as it is a crucial and common knowledge process that underpins many KM initiatives. The future state of search is purposely elaborated in the context of smart KM together with a reframing of cloud computing, which leads to the concept of a knowledge cloud that acts as an e-canvas supporting dynamic capabilities, knowledge/expertise location, harnessing, and assembling new business models for the orchestration and delivery of knowledge services. Prototypical systems on peer-based personal lifelong learning and an intelligent workbench that facilitates chance discovery via serendipity over topic maps are presented as a glimpse to what future KMS will take shape.
Visualising Knowledge for Decision-Making: A Framework for Selecting Visual Templates

Digital technologies and AI have led to an increase in the automation of work, resulting in computers solving structured problems, while humans are responsible for ill-structured problem-solving, now and especially in the future. Several visual collaboration and knowledge structuring tools, such as Miro, Visio, and Lucidcharts, can help managers and experts to analyse ill-structured problems and co-create solutions. However, selecting the appropriate knowledge visualisation template for a comprehensive description and representation of knowledge remains an open research field. Although well-designed visual representations can improve decision-making, they can also introduce bias if not well conceived. They may constrain the attention to a limited set of decision variables, highlight only less important variables, alter the salience of knowledge, or inspire inappropriate comparisons. The use of well-conceived visual templates can reduce this risk by being easy to use, facilitating pattern recognition, and providing means for knowledge transfer, sharing, codification, and creation. This chapter explores the use of visual templates to support knowledge management activities, problem-solving, and decision-making. It suggests a new approach to the selection of visual templates, which support the decision-making process, and proposes a framework to help scholars and practitioners select the appropriate visual template for representing knowledge associated with a problem, taking into account the level of formality, knowledge type, and other dimensions. It also introduces new criteria for choosing visual knowledge templates, e.g. mental scenarios, knowledge content, and domain dependence. Given the exponential development of AI, the anticipated future steps in knowledge management research and practice are associated with a combination of visual knowledge structuring and AI-driven content recommendations and assistance.
Quantum simulation of ODEs, PDEs and related problems. Part I: Schrodingerisation of linear systems

One of the oldest and currently most promising application areas for quantum devices is quantum simulation. Popularised by Feynman in the early 1980s, it is important for the efficient simulation – compared to its classical counterpart – of one special partial differential equation (PDE): Schrodinger’s equation. This is possible because quantum devices themselves naturally obey Schrodinger’s equation. Just like with large-scale quantum systems, classical methods for other high-dimensional and large-scale PDEs often suffer from the curse-of-dimensionality, which a quantum treatment might in certain cases be able to mitigate. Aside from Schrodinger’s equation, can quantum simulators also efficiently simulate other PDEs? To enable the simulation of PDEs on quantum devices that obey Schrodinger’s equations, it is crucial to first develop good methods for mapping other PDEs onto Schrodinger’s equations. After a brief introduction to quantum simulation, I will address the above question in this talk by introducing a simple and natural method for mapping other linear PDEs onto Schrodinger’s equations. It turns out that by transforming a linear partial differential equation (PDE) into a higher-dimensional space, it can be transformed into a system of Schrodinger’s equations, which is the natural dynamics of quantum devices. This new method – called Schrodingerisation – thus allows one to simulate, in a simple way, any general linear partial differential equation and system of linear ordinary differential equations via quantum simulation. This simple methodology is also very versatile. It can be used directly either on discrete-variable quantum systems (qubits) or on analog/continuous quantum degrees of freedom (qumodes). The continuous representation in the latter case can be more natural for PDEs since, unlike most computational methods, one does not need to discretise the PDE first. In this way, we can directly map D-dimensional linear PDEs onto a (D + 1)-qumode quantum system where analog Hamiltonian simulation on (D + 1) qumodes can be used. It is the quantum version of analog computing and is more amenable to near-term realisation. I will show how this Schrodingerisation method can be applied to linear PDEs, systems of linear ODEs and also linear PDES with random coefficients, where the latter is important in the area of uncertainty quantification. Furthermore, these methods can be extended to solve problems in linear algebra by transforming iterative methods in linear algebra into the evolution of linear ODEs.
Business Disruption on TikTok: Quiet Quitting Challenging Corporate Social Responsibility Policies

During the past three years, the world has borne witness to the most significant disruptor in decades: the COVID-19 pandemic. Collateral effects have been vast including significant changes in the way collaborators have decided to prioritize their wellbeing. In addition, workers around the world are increasingly less tolerant to organizations’ performative actions, and both have led to mass resignation and quiet quitting. This conceptual study discusses a series of propositions and a conceptual framework and contributes to previous literature related to corporate social responsibility, collaborator management, as well as social media management.
Utilizing the intelligent sensing function of conjugated materials to create intelligent flute training equipment

The manufacturing of flutes has always relied on traditional ceramics and metals, which may be affected by various factors during the manufacturing process, as well as the lack of intelligent sensing functions, resulting in poor sound quality and performance of the instrument. The purpose of this article is to explore the use of the intelligent sensing function of conjugated materials to create intelligent flute training equipment, achieve automatic tuning, volume control, etc., and improve the playing experience and training effect of the instrument. This article first analyzes the smart sensing function of conjugate materials and applies it to smart flute training equipment; then, introduces photosensitive materials at appropriate locations to change the size and shape of the flute's sound hole, thereby adjusting the timbre; finally, uses smart flute training based on conjugate materials for real-time perception of performers' performance experiments. The test results show that the average delay time of the conjugate material trench is reduced by 73.1% compared with the average delay time of the ceramic trench, and is reduced by 63.5% compared with the average delay time of the metal trench. This shows that the conjugated material flute is more intelligent and can quickly respond to the player's performance and automatically control and respond.
Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Game theory can employ reinforcement learning algorithms to identify the optimal policy or equilibrium solution. Potential-based reward shaping (PBRS) methods are prevalently used for accelerating reinforcement learning, ensuring the optimal policy remains consistent. Existing PBRS research performs message passing based on graph convolution neural networks (GCNs) to propagate information from rewarding states. However, in an irreversible time-series reinforcement learning problem, undirected graphs will not only mislead message-passing schemes but also lose a distinctive direction structure. In this paper, a novel approach called directed graph convolution neural networks for reward shaping φ DCN has been proposed to tackle this problem. The key innovation of φ DCN is the extension of spectral-based undirected graph convolution to directed graphs. Messages can be efficiently propagated by leveraging a directed graph Laplacian as a substitute for the state transition matrix. As a consequence, potential-based reward shaping can then be implemented by the propagated messages. The incorporation of temporal dependencies between states makes φ DCN more suitable for real-world scenarios than existing potential-based reward shaping methods based on undirected graph convolutional networks. Preliminary experiments demonstrate that the proposed φ DCN exhibits a substantial improvement compared to other competing algorithms on both Atari and MuJoCo benchmarks.
Possibilistic distribution distance metric: a robust domain adaptation learning method

The affective Brain-Computer Interface (aBCI) systems, which achieve predictions for individual subjects through training on multiple subjects, often cannot achieve satisfactory results due to the differences in Electroencephalogram (EEG) patterns between subjects. One tried to use Subject-specific classifiers, but there was a lack of sufficient labeled data. To solve this problem, Domain Adaptation (DA) has recently received widespread attention in the field of EEG-based emotion recognition. Domain adaptation (DA) learning aims to solve the problem of inconsistent distributions between training and test datasets and has received extensive attention. Most existing methods use Maximum Mean Discrepancy (MMD) or its variants to minimize the problem of domain distribution inconsistency. However, noisy data in the domain can lead to significant drift in domain means, which can affect the adaptability performance of learning methods based on MMD and its variants to some extent. Therefore, we propose a robust domain adaptation learning method with possibilistic distribution distance measure. Firstly, the traditional MMD criterion is transformed into a novel possibilistic clustering model to weaken the influence of noisy data, thereby constructing a robust possibilistic distribution distance metric (P-DDM) criterion. Then the robust effectiveness of domain distribution alignment is further improved by a fuzzy entropy regularization term. The proposed P-DDM is in theory proved which be an upper bound of the traditional distribution distance measure method MMD criterion under certain conditions. Therefore, minimizing P-DDM can effectively optimize the MMD objective. Secondly, based on the P-DDM criterion, a robust domain adaptation classifier based on P-DDM (C-PDDM) is proposed, which adopts the Laplacian matrix to preserve the geometric consistency of instances in the source domain and target domain for improving the label propagation performance. At the same time, by maximizing the use of source domain discriminative information to minimize domain discrimination error, the generalization performance of the learning model is further improved. Finally, a large number of experiments and analyses on multiple EEG datasets (i.e., SEED and SEED-IV) show that the proposed method has superior or comparable robustness performance (i.e., has increased by around 10%) in most cases.