Reviewerly/exHarmony huggingface dataset:
SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Hand gesture recognition (HGR) is a rapidly evolving field with the potential to revolutionize human–computer interactions by enabling machines to interpret and understand human gestures for intuitive communication and control. However, HGR faces challenges such as the high similarity of hand gestures, real-time performance, and model generalization. To address these challenges, this paper proposes the stacking of distilled vision transformers, referred to as SDViT, for hand gesture recognition. An initially pretrained vision transformer (ViT) featuring a self-attention mechanism is introduced to effectively capture intricate connections among image patches, thereby enhancing its capability to handle the challenge of high similarity between hand gestures. Subsequently, knowledge distillation is proposed to compress the ViT model and improve model generalization. Multiple distilled ViTs are then stacked to achieve higher predictive performance and reduce overfitting. The proposed SDViT model achieves a promising performance on three benchmark datasets for hand gesture recognition: the American Sign Language (ASL) dataset, the ASL with digits dataset, and the National University of Singapore (NUS) hand gesture dataset. The accuracies achieved on these datasets are 100.00%, 99.60%, and 100.00%, respectively.
Knowledge Distillation for Enhancing a Lightweight Magnet Tile Target Detection Model: Leveraging Spatial Attention and Multi-Scale Output Features

Accurate and efficient sorting of diverse magnetic tiles during manufacturing is vital. However, challenges arise due to visual similarities among types, necessitating complex computer vision algorithms with large sizes and high computational needs. This impedes cost-effective deployment in the industry, resulting in the continued use of inefficient manual sorting. To address this issue, we propose an innovative lightweight magnetic tile detection approach that improves knowledge distillation for a compressed YOLOv5s model. Incorporating spatial attention modules into different feature extraction stages of YOLOv5s during the knowledge distillation process can enhance the ability of the compressed model to learn the knowledge of intermediate feature extraction layers from the original large model at different stages. Combining different outputs to form a multi-scale output, the multi-scale output feature in the knowledge refinement process enhances the capacity of the compressed model to grasp comprehensive target knowledge in outputs. Experimental results on our self-built magnetic tile dataset demonstrate significant achievements: 0.988 mean average precision, 0.5% discrepancy compared to the teacher’s network, and an 85% model size reduction. Moreover, a 36.70% boost in inference speed is observed for single image analysis. Our method’s effectiveness is also validated by the Pascal VOC dataset results, showing potential for broader target detection scenarios. This approach offers a solution to magnetic tile target detection challenges while being expected to expand to other applications.
Stereo Matching Algorithm of Multi-Feature Fusion Based on Improved Census Transform

This article proposes an improved stereo matching algorithm in order to address the issue that the conventional Census transform is overly dependent on the center pixel of the window, which makes the algorithm susceptible to noise interference and results in low matching accuracy in regions with weak texture and complex texture. In the cost calculation stage, the noise threshold is set utilizing the absolute difference detection approach, and pixels that exceed the threshold are replaced with the mean gray values of the neighboring pixels in the 3 × 3 window. This stage also includes the introduction of the gradient cost, which is coupled with the edge and feature point information to provide the final matching cost. The cross approach is employed to build the adaptive support domain and aggregate the costs during the cost aggregation stage. The disparity is finally calculated using the WTA technique, and a multi-step refinement process is employed to produce the final disparity map. The experiments demonstrate that the proposed algorithm has good anti-noise performance. Compared with other improved algorithms or composite algorithms, the average matching rate of the four standard images on the Middlebury test platform is 5.53%, which is higher than the remaining algorithms, indicating that the matching accuracy is high. The proposed algorithm provides ideas for subsequent improved algorithms.
Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform

An improved Hybrid Collaborative Filtering algorithm (H-CF) is proposed, addressing the issues of data sparsity, low recommendation accuracy, and poor scalability present in traditional collaborative filtering algorithms. The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model (LFM) and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm (ITCSCF). To begin with, the items are clustered based on their attribute dimension, which accelerates the computation of the nearest neighbor set. Subsequently, H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items. This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness. Furthermore, a weighting function is employed to combine the various improved algorithms. The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list. To address the real-time and scalability concerns, the algorithm leverages the Spark big data distributed cluster computing framework. Experiments were conducted using the public dataset MovieLens, where the improved algorithm's performance was compared against the algorithm before enhancement and the algorithm running on a single machine. The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity, recommendation personalization, accuracy, recall, and efficiency.
Dynamical Neural Network Based on Spin Transfer Nano-Oscillators

Spintronic technology promises to significantly increase the efficiency and scalability of neural networks by employing optimized task-oriented device components that exhibit intrinsic nonlinearity, temporal nonlocality, scalability, and electrical tunability. In particular, the functional response of spin-transfer torque oscillators can be designed to naturally emulate the building blocks of neural networks, such as short-term memory, hierarchy, and nonlinearity. We propose spin-transfer nano-oscillators as a dynamic neuron that can be used in a neural network coupled with a fully connected layer to perform classification tasks. In this concept, successive nodes of the neural network correspond to successive time steps, so that the nonlinearity and memory of the system can be naturally exploited. The tunability of the device allows to project initial configurations in well-defined regions of the phase space where classification is easily performed. Furthermore, training is performed using optimal control theory. We emphasize that the devices benefit from more realistic models compared to simpler analytical models and is robust against device-to-device variations. We tested the performance of the network on two types of datasets and obtained 99% accuracy. Although these systems are computationally expensive, their hardware implementation is simple and inexpensive.
Biological Mineral Recovery Geothermal Fluid

It is possible to extract valuable metals from the fluid by using biological techniques of metal recovery from geothermal fluids. These methods include the use of microorganisms. Geothermal fluids provide the potential for the recovery of a wide variety of metals, including but not limited to silica, lithium, zinc, and manganese, amongst others. Utilizing microorganisms in this context is known as bio extraction or biomining. Not all geothermal fluids have commercially relevant amounts of metals, and the composition of these fluids may vary greatly depending on the geological location. The efficiency and efficacy of these biological procedures are also affected by details like the kind of microorganisms used, the fluid׳s metal content, and the machine׳s settings. Therefore, more study and optimization are needed to make these methods cost-effective for metal recovery from geothermal fluids.
Accelerating Data-Intensive Seismic Research Through Parallel Workflow Optimization and Federated Cyberinfrastructure

Earthquake early warning systems use synthetic data from simulation frameworks like MudPy to train models for predicting the magnitudes of large earthquakes. MudPy, although powerful, has limitations: a lengthy simulation time to generate the required data, lack of user-friendliness, and no platform for discovering and sharing its data. We introduce FakeQuakes DAGMan Workflow (FDW), which utilizes Open Science Grid (OSG) for parallel computations to accelerate and streamline MudPy simulations. FDW significantly reduces runtime and increases throughput compared to a single-machine setup. Using FDW, we also explore partitioned parallel HTCondor DAGMan workflows to enhance OSG efficiency. Additionally, we investigate leveraging cyberinfrastructure, such as Virtual Data Collaboratory (VDC), for enhancing MudPy and OSG. Specifically, we simulate using Cloud bursting policies to enforce FDW job-offloading to VDC during OSG peak demand, addressing shared resource issues and user goals; we also discuss VDC’s value in facilitating a platform for broad access to MudPy products.
An End-to-End HPC Framework for Dynamic Power Objectives

High-Performance Computing (HPC) centers demand a lot of power, and continue to grow through the Exascale era. This work establishes the need for a multi-tiered, feedback-driven power management framework to follow dynamic power objectives while maximizing job performance, highlighting the need to respond to external factors (e.g., power constraints), and internal factors (e.g., performance variation). We present a practical implementation of this framework on a real-world cluster in addition to conducting simulations for larger data centers. We accurately track a moving power target for demand response while reacting to incomplete or inaccurate prior knowledge about job power and performance properties. We demonstrate that online performance feedback from a job runtime enables a cluster power management policy to recover most of the performance degradation introduced by job-type misclassification.
Distributed Data Locality-Aware Job Allocation

Scheduling tasks close to their associated data is crucial in distributed systems to minimize network traffic and latency. Some Big Data frameworks like Apache Spark employ locality functions and job allocation algorithms to minimize network traffic and execution times. However, these frameworks rely on centralized mechanisms, where the master node determines data locality by allocating tasks to available workers with minimal data transfer time, ignoring variances in worker configurations and availability. To address these limitations, we propose a decentralized approach to locality-driven scheduling that grants workers autonomy in the job allocation process while factoring in workers’ configurations, such as network and CPU speed differences. Our approach is developed and evaluated on Crossflow, a distributed stream processing platform with data-aware independent worker nodes. Preliminary evaluation experiments indicate that our approach can yield up to 3.57x faster execution times when compared to the baseline centralized approach where the master controls data locality.
PM100: A Job Power Consumption Dataset of a Large-scale Production HPC System

The power requirements of modern High-Performance Computing (HPC) systems pose environmental and financial challenges, as they contribute to carbon emissions and strain power grids. Optimizing power consumption together with system performance has thus become a crucial goal for HPC centers. As jobs running on a system contribute to the whole system's power usage, predicting their power requirements before execution on the system would allow to forecast the overall power consumption and perform techniques like power capping at the workload manager level. Such predictive studies need quality data, which is limited due to the inherent complexity of collecting structured data for job power characterization in a production system. This paper aims to fill the lack of resources for job power prediction and provide the HPC community with (i) a methodology to create a job power consumption dataset from workload manager data and node power metrics logs, and (ii) a novel and large dataset comprising around 230K jobs and their corresponding power consumption values. The dataset is derived from M100, a holistic dataset extracted from a production supercomputer hosted at the HPC centre CINECA in Italy.
Fast Simulation of High-Depth QAOA Circuits

Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA). Our simulator is designed with the goal of reducing the computational cost of QAOA parameter optimization and supports both CPU and GPU execution. Our central observation is that the computational cost of both simulating the QAOA state and computing the QAOA objective to be optimized can be reduced by precomputing the diagonal Hamiltonian encoding the problem. We reduce the time for a typical QAOA parameter optimization by eleven times for n = 26 qubits compared to a state-of-the-art GPU quantum circuit simulator based on cuQuantum. Our simulator is available on GitHub: https://github.com/jpmorganchase/QOKit
DPU Offloading Programming with the OpenMP API

Data processing units (DPUs) as network co-processors are an emerging trend in our community, with plenty of opportunities yet to be explored. These have been generally used as domain-specific accelerators transparent to application developers; In the HPC field, DPUs have been used as MPI accelerators, but also to offload some tasks from the general-purpose processor. However, the latter required application developers to deploy MPI ranks in the DPUs, as if they were remote (weak) compute nodes, hence considerably hindering programmability. The wide adoption of OpenMP as the threading model in the HPC arena, along with that of GPU accelerators, is making OpenMP offloading to GPUs a wide trend for HPC applications. In this paper we introduce, for the first time in the literature, OpenMP offloading support for network co-processor DPUs. We present our design in LLVM to support OpenMP standard offloading semantics and discuss the programming productivity advantages with respect to the existing MPI-based programming model. We also provide the corresponding performance analysis demonstrating competitive results in comparison with the MPI baseline.
Revolutionising HRM practice with blockchain technology: unleashing disruptive paradigms of work and overcoming management challenges

Blockchain technology is anticipated to have a significant impact on human resource management (HRM) in the era of Industry 4.0. This study employs a literature review methodology and examines 42 relevant publications to identify key areas where blockchain technology can be applied in HRM. The findings reveal several crucial applications of Blockchain in HRM. Firstly, blockchain technology can effectively prevent interview fraud and enhance the reliability of the recruitment process. Secondly, it can improve training and certification processes, address pay equity issues, ensure fair performance evaluations, and facilitate employee relations. These applications are vital in enhancing the efficiency and accuracy of HRM practices. However, the study also highlights certain management concerns related to adopting blockchain technology, such as management's commitment, stakeholder collaboration, and data breach risks. Building upon these findings, a comprehensive discussion is initiated, summarising the contributions and critical findings of the research and emphasising the potential advantages of integrating blockchain technology into HRM. Additionally, the study examines the contributions made by existing literature and proposes directions for future research. Finally, a research agenda is put forth to explore the effective implementation of blockchain technology in practical HRM.
The CASA theory no longer applies to desktop computers

Abstract The Computers Are Social Actors (CASA) theory is the most important theoretical contribution that has shaped the field of human–computer interaction. The theory states that humans interact with computers as if they are human, and is the cornerstone on which all social human–machine communication (e.g., chatbots, robots, virtual agents) are designed. However, the theory itself dates back to the early 1990s, and, since then, technology and its place in society has evolved and changed drastically. Here we show, via a direct replication of the original study, that participants no longer interact with desktop computers as if they are human. This suggests that the CASA Theory may only work for emergent technology, an important concept that needs to be taken into account when designing and researching human–computer interaction.
Provable lattice reduction of $$\mathbb {Z}^n$$ with blocksize n/2

Abstract The Lattice Isomorphism Problem (LIP) is the computational task of recovering, assuming it exists, an orthogonal linear transformation sending one lattice to another. For cryptographic purposes, the case of the trivial lattice $$\mathbb Z^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>Z</mml:mi> <mml:mi>n</mml:mi> </mml:msup> </mml:math> is of particular interest ( $$\mathbb {Z}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Z</mml:mi> </mml:math> LIP). Heuristic analysis suggests that the BKZ algorithm with blocksize $$\beta = n/2 + o(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>β</mml:mi> <mml:mo>=</mml:mo> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> solves such instances (Ducas, Postlethwaite, Pulles, van Woerden, ASIACRYPT 2022). In this work, I propose a provable version of this statement, namely, that $$\mathbb {Z}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Z</mml:mi> </mml:math> LIP can indeed be solved by making polynomially many calls to a Shortest Vector Problem oracle in dimension at most $$n/2 + 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> .
Machine Learning based Noise Characterization and Correction on Neutral Atoms NISQ Devices

Abstract Neutral atoms devices represent a promising technology using optical tweezers to geometrically arrange atoms and modulated laser pulses to control their quantum states. They are exploited as noisy intermediate‐scale quantum (NISQ) processors. Indeed, like all real quantum devices, they are affected by noise introducing errors in the computation. Therefore, it is important to understand and characterize the noise sources and possibly to correct them. Here, two machine‐learning based approaches are proposed respectively to estimate the noise parameters and to mitigate their effects using only measurements of the final quantum state. Our analysis is then tested on a real neutral atom platform, comparing our predictions with a priori estimated parameters. It turns out that increasing the number of atoms is less effective than using more measurements on a smaller scale. The agreement is not always good but this may be due to the limited amount of real data that are obtained from a still under development device. Finally, reinforcement learning is employed to design a pulse that mitigates the noise effects. Our machine learning‐based approach is espected to be very useful for the noise benchmarking of NISQ processors and, more in general, of real quantum technologies.
Real-time prediction of distance and PGA from <i>P</i>-wave features using Gradient Boosting Regressor for on-site earthquake early warning applications

SUMMARY On-site earthquake early warning (EEW) systems represent an important way to reduce seismic hazard. Since these systems are fast in providing an alert and reliable in the prediction of the ground motion intensity at targets, they are particularly suitable in the areas where the seismogenic zones are close to cities and infrastructures, such as Central Italy. In this work, we use Gradient Boosting Regressor (GBR) to predict peak ground acceleration (PGA), and hypocentral distance (D) starting from P-wave features. We use two data sets of waveforms from two seismic sequences in Central Italy: L'Aquila sequence (2009) and the Amatrice–Norcia–Visso sequence (2016–2017), for a total of about 80 000 three-component waveforms. We compute 60 different features related to the physics of the earthquake using three different time windows (1 s, 2 s and 3 s). We validate and train our models using the 2016–2017 data sets (the bigger one) and we test it on the 2009 data set. We study the performances of GBR predicting D and PGA in terms of prediction scores, finding that the models can well predict both targets even using 1 s window, and that, as expected, the results improve using longer time windows. Moreover, we perform a residual analysis on the test set finding that the PGA can be predicted without any bias, while the D prediction presents a correlation with the moment magnitude. In the end, we propose a prototype for a probabilistic on-site EEW system based on the prediction of D and PGA. The proposed system is a threshold-based approach and it releases an alert on four possible levels, from 0 (far and small event) to 3 (close and strong event). The system computes the probability related to each alert level. We test two different set of thresholds: the Felt Alert and the Damage Alert. Furthermore, we consider the lead time (LT) of the PGA to distinguish between useful alerts (positive LT) and Missed Alerts (MA). In the end, we analyse the performance of such a system considering four possible scenarios: Successful Alert (SA), Missed Alert (MA), Overestimated Alert (OA) and Underestimated Alert (UA). We find that the system obtains SA rate about 80 per cent at 1 s, and that it decreases to about 65 per cent due to the increase in MA. This result shows how the proposed system is already reliable at 1 s, which would be a huge advantage for seismic prone regions as Central Italy, an area characterized by moderate-to-large earthquakes (Mw &amp;lt; 7).