Reviewerly/exHarmony huggingface dataset:
An intelligent distribution system for green logistics operations in the blockchain environment

To ensure the safety of fresh food, the cold chain logistics is widely utilised, which can provide a low temperature environment. However, due to the high energy consumption, how to reduce carbon emission of cold chain logistics to develop sustainable distribution has become the research focus. In response, this study proposes an intelligent distribution system for cold chain logistics to achieve green operations. Specifically, the framework of the intelligent system based on blockchain technology is proposed to improve operational efficiency and enhance food traceability. Meanwhile, an electric vehicle routing optimisation model taking into account carbon emission is deployed in the intelligent system to plan the distribution routes. The suggested optimisation model seeks to reduce the total cost, including fixed, spoilage, refrigeration, punishment, queuing, charging and carbon emission costs. Furthermore, an ant colony algorithm is embedded in the intelligent system to help with distribution routes design. Finally, this study combines a real case to discuss the performance of the intelligent system, and the results show that the proposed system can significantly reduce distribution cost and carbon emission. Managers can use the suggested intelligent system to design distribution routes and monitor the distribution process.
Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks.
Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning strategy based on evolutionary deep reinforcement learning. The low sampling efficiency of the reinforcement learning algorithm is improved by evolutionary learning. The combination of these two components as a new algorithm has become a current research trend. We first combined the evolutionary algorithm with different reinforcement learning algorithms to verify the effectiveness of the combination of algorithms with different strategies. Experimental results indicate that the fusion of the two algorithms based on a maximum-entropy strategy is more effective for adaptive sampling using a 3D marine observation platform. Data assimilation experiments indicate that adaptive sampling data from a 3D mobile observation platform based on evolutionary deep reinforcement learning improves the accuracy of marine environment numerical prediction systems.
Error-mitigated quantum simulation of interacting fermions with trapped ions

Abstract Quantum error mitigation has been extensively explored to increase the accuracy of the quantum circuits in noisy-intermediate-scale-quantum (NISQ) computation, where quantum error correction requiring additional quantum resources is not adopted. Among various error-mitigation schemes, probabilistic error cancellation (PEC) has been proposed as a general and systematic protocol that can be applied to numerous hardware platforms and quantum algorithms. However, PEC has only been tested in two-qubit systems and a superconducting multi-qubit system by learning a sparse error model. Here, we benchmark PEC using up to four trapped-ion qubits. For the benchmark, we simulate the dynamics of interacting fermions with or without spins by applying multiple Trotter steps. By tomographically reconstructing the error model and incorporating other mitigation methods such as positive probability and symmetry constraints, we are able to increase the fidelity of simulation and faithfully observe the dynamics of the Fermi–Hubbard model, including the different behavior of charge and spin of fermions. Our demonstrations can be an essential step for further extending systematic error-mitigation schemes toward practical quantum advantages.
Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Representation learning or feature learning refers to a collection of methods employed in machine learning, which allows systems to autonomously determine representations needed for classifications or feature detection from unprocessed data. Representation learning algorithms are specifically crafted to acquire knowledge of conceptual features that define data. The field of state representation learning is centered on a specific type of representation learning that involves the acquisition of low-dimensional learned features that undergo temporal evolution and are subject to the influence of an agent's actions. Over the past few years, deep architecture have been widely employed for representation learning and have demonstrated exceptional performance in various tasks, including but not limited to object detection, speech recognition, and image classification. This article provides a comprehensive overview of the evolution of techniques for data representation learning. Our research focuses on the examination of conventional feature learning algorithms and advanced deep learning models. This paper presents an introduction to data representation learning history, along with a comprehensive list of available resources such as online courses, tutorials, and books. Additionally, various tool-boxes are also provided for further exploration in this field. In conclusion, this article presents remarks and future prospects for data representation learning.
A encoder-decoder deblurring network combined with high-frequency a priori

Abstract Convolutional neural network based methods have been proposed to address blind deblurring. Aiming at the difficulty of reconstructing high-frequency features of images with existing network models, this paper proposes a two-stage convolution-based encoder-decoder for fusing high-frequency a prior. In the first stage, both blurred and high-frequency images are input, and the image features extracted by the network and the high-frequency features are fused. In the second stage, the fused features are further refined and recovered as potentially clear images, and the reconstruction ability of the model for high-frequency features is enhanced. In addition, this paper proposes a deep feature reorganization module that integrates multi-layer semantic information in the encoder-decoder and targets the encoder semantics to further enhance the feature characterization capability of the model. Comprehensive experimental results show that our method achieves 0.9085 structural similarity index (SSIM) and 30.66db peak signal-to-noise ratio (PSNR) on the GoPro dataset. Meanwhile, our method achieves 0.8514 SSIM and 27.39db PSNR on the Lai dataset.
Corporate Digital Responsibility and the Business Implications of Quantum Computing

While artificial intelligence has been attracting increasing investment and controversy, a new, and potentially much more powerful technology, Quantum Computing, is on the business horizon. Already, organizations are assessing its potential for addressing a range of computationally challenging issues, such as last-mile optimization and cryptanalysis to break encryption algorithms, and are exploring possible applications in a range of business areas. While the advent of Quantum Computing may bring an array of opportunities and benefits, the companies developing and applying Quantum Computing technologies will face, and have to address, new sets of corporate social, ethical, economic, technical and environmental responsibilities. Using a qualitative research method based on secondary sources, this article examines the implications for Quantum Computing for corporate digital responsibility, and assesses the guidance currently being offered by leading consultancies for organisations deploying Quantum Computing. The results suggest that although there is some guidance on how companies can apply and exploit Quantum Computing technology, there is as yet little consideration of the possible downsides, including, for example, potential negative environmental impacts. Further research is needed to develop a more balanced assessment of benefits and disbenefits as Quantum Computing is deployed in the corporate world and wider global society.
Real-Time Object Detection and Tracking for Unmanned Aerial Vehicles Based on Convolutional Neural Networks

This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system’s feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments.
Reconstructing higher-resolution four-dimensional time-varying volumetric data

We have witnessed substantial growth in super-resolution research within the computer vision community. Unlike previous works that mainly focus on the super-resolution synthesis of images, videos, or single volumes, our research is dedicated to the super-resolution synthesis of time-varying volumetric data, which are generated from scientific simulations and are crucial for domain scientists to understand and analyse complex scientific phenomena. Compared to previous works, our research presents a greater challenge: the time-varying volumetric data have higher dimensions, making it more difficult to synthesise super-resolution that maintains good spatio-temporal consistency while achieving high visual quality. To tackle this challenge, we introduce a new GAN-based network called SSR-DoubleUNetGAN, which includes novel network architecture and loss functions, allowing for accurate synthesis of spatial super-resolution for time-varying volumetric data with relatively fast training time. Our method can be applied in the context of in-situ visualisation to aid domain scientists in analysing more time-varying volumetric data more efficiently. In addition, it can be used in the compression-decompression pipeline to recover the super-resolution time-varying volumetric data from their low-resolution counterpart. To demonstrate its effectiveness, we applied various time-varying volumetric datasets from different scientific simulations to it. To demonstrate its advantages, we compared it qualitatively and quantitatively with five state-of-the-art super-resolution techniques, namely SSR-TVD, Tricubic, SRResNet, Cubic, and Linear. Furthermore, we conducted an ablation study to validate its important modules. The experimental results show that our method outperforms the compared state-of-the-art techniques.
Prediction-driven resource provisioning for serverless container runtimes

In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, as (i) real-world FaaS workloads may exhibit highly dynamic request patterns, (ii) applications have service-level objectives (SLOs) that must be met, and (iii) container provisioning can be a costly process. In this paper, we present SLOPE, a prediction framework for serverless FaaS platforms to address the aforementioned challenges. Specifically, it trains a neural network model that utilizes knowledge from past runs in order to estimate the number of instances required to satisfy the invocation rate requirements of the serverless applications. In cases that a priori knowledge is not available, SLOPE makes predictions using a graph edit distance approach to capture the similarities among serverless applications. Our experimental results illustrate the efficiency and benefits of our approach, which can reduce the operating costs by 66.25% on average.
CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning

Autonomous navigation in off-road environments has been extensively studied in the robotics field. However, navigation in covert situations where an autonomous vehicle needs to remain hidden from outside observers remains an under-explored area. In this paper, we propose CoverNav, a novel Deep Reinforcement Learning (DRL) based algorithm, for identifying covert and navigable trajectories with minimal cost in off-road terrains and jungle environments in the presence of observers. CoverNav focuses on unmanned ground vehicles seeking shelters and taking covers while safely navigating to a predefined destination. Our proposed DRL method computes a local cost map that helps distinguish which path will grant the maximal covertness while maintaining a low-cost trajectory using an elevation map generated from 3D point cloud data, the robot’s pose, and directed goal information. If an observer is spotted, CoverNav enables the robot to select natural obstacles (e.g., rocks, houses, trees, etc.) and use them as shelters to hide behind. We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL-based navigation algorithm. Additionally, we evaluate CoverNav’s effectiveness in achieving a maximum goal distance of 12 meters and its success rate in different elevation scenarios with and without cover objects. We observe competitive performance comparable to state-of-the-art (SOTA) methods without compromising accuracy.
Metaverse: A Young Gamer's Perspective

When developing technologies for the Metaverse, it is important to understand the needs and requirements of end users. Relatively little is known about the specific perspectives on the use of the Metaverse by the youngest audience: children ten and under. This paper explores the Metaverse from the perspective of a young gamer. It examines their understanding of the Metaverse in relation to the physical world and other technologies they may be familiar with, looks at some of their expectations of the Metaverse, and then relates these to the specific multimedia signal processing (MMSP) research challenges. The perspectives presented in the paper may be useful for planning more detailed subjective experiments involving young gamers, as well as informing the research on MMSP technologies targeted at these users.
The roles, challenges, and merits of the p value

Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.
Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients

The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AICare outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships and variations in feature importance and provides reference values. An AI-doctor interaction system is developed for visualizing patients' health trajectories and risk indicators.
LWE‐based verifiable essential secret image sharing scheme ((t,s,k,n)$( {t,s,k,n} )$ ‐ VESIS)

Abstract In traditional secret image sharing schemes, shareholders have similar situations, and to recover the secret image, they must be present as many as the threshold. However, in scenarios where the shareholders occupy different positions, essential and non‐essential, the use of essential secret image sharing schemes becomes necessary. This article presents a novel verifiable essential secret image sharing scheme based on LWEs that incorporates Bloom filters and hash functions for verification purposes. The proposed scheme for a secret image does not require any pre‐processing. The shadow images of both essential and non‐essential shareholders are of the same size, eliminating the need for concatenating sub‐shadows. Furthermore, shadow images for essential and non‐essential shareholders are produced simultaneously in a single step. The scheme is also quantum safe thanks to the use of quantum‐resistant primitives. The experimental results confirm the security and efficiency of the proposed scheme.
Classically optimized variational quantum eigensolver with applications to topological phases

The variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithms for near-term quantum computers. Meanwhile, VQE is confronted with a challenge that statistical error associated with measurement as well as systematic error could significantly hamper the optimization. To circumvent this issue, we propose the classically optimized VQE (CO-VQE), where the whole process of optimization is efficiently conducted on a classical computer. The efficacy of the method is guaranteed by the observation that quantum circuits with up to logarithmic depth are classically tractable via simulations of local subsystems with up to quasipolynomial cost (polynomial for constant depth). In CO-VQE, we only use quantum computers to measure nonlocal quantities after the parameters are optimized. As a proof of concept, we present numerical experiments on quantum spin models with topological phases. After the optimization, we identify the topological phases by nonlocal order parameters as well as unsupervised machine learning on inner products between quantum states. The proposed method maximizes the advantage of using quantum computers while avoiding strenuous optimization on noisy quantum devices. In addition, our paper indicates that clustering technique combined with the fidelity measured on quantum computers could be useful for phase classification in condensed-matter physics.
Integrating Lorenz Hyperchaotic Encryption with Ring Oscillator Physically Unclonable Functions (RO-PUFs) for High-Throughput Internet of Things (IoT) Applications

With the combined call for increased network throughput and security comes the need for high-bandwidth, unconditionally secure systems. Through the combination of true random number generators (TRNGs) for unique seed values, and four-dimensional Lorenz hyperchaotic systems implemented on a Stratix 10 Intel FPGA, we are able to implement 60 MB/s encryption/decryption schemes with 0% data loss on an unconditionally secure system with the NIST standard using less than 400 mW. Further, the TRNG implementation allows for unique encryption outputs for similar images while still enabling proper decryption. Histogram and adjacent pixel analysis on sample images demonstrate that without the key, it is not possible to extract the plain text from the encrypted image. This encryption scheme was implemented via PCIe for testing and analysis.
Resource-Adaptive Newton’s Method for Distributed Learning

By leveraging curvature information for improved performance, Newton’s method offers significant advantages over first-order methods for distributed learning problems. However, the practical applicability of Newton’s method is hindered in large-scale and heterogeneous learning environments due to challenges such as high computation and communication costs associated with the Hessian matrix, sub-model diversity, staleness in training, and data heterogeneity. To address these challenges, this paper introduces a novel and efficient algorithm called Resource-Adaptive Newton Learning (RANL), which overcomes the limitations of Newton’s method by employing a simple Hessian initialization and adaptive assignments of training regions. The algorithm demonstrates impressive convergence properties, which are rigorously analyzed under standard assumptions in stochastic optimization. The theoretical analysis establishes that RANL achieves a linear convergence rate while effectively adapting to available resources and maintaining high efficiency. Moreover, RANL exhibits remarkable independence from the condition number of the problem and eliminates the need for complex parameter tuning. These advantages make RANL a promising approach for distributed learning in practical scenarios.