Reviewerly/exHarmony huggingface dataset:
Myth or reality? The Digitalization of Climate-Smart Agriculture (DCSA) practices in smallholding agriculture in the Bono East Region of Ghana

Digitalization of Climate-Smart Agriculture practices leverages the power of digital agriculture tools/services (DATs) of any form (hardware, software, or data) in Climate-smart agriculture (CSA) practices to promote enhanced adaptation, GHGs emissions mitigation and increase productivity for smallholding agriculture. This research used a mixture of participatory and learning approaches with an emphasis on Expert Interviews and a Large-scale Household Survey involving 1219 farmers in the Bono East of Ghana to assess the awareness and utilization of DATs in smallholder farmers' CSA practices. Precisely, we assess farmers' engagement with Digital Agriculture Services (DAS) and DAT such as; TVs, Radios, Mobile phones/Tablets, Unmanned Aerial Vehicles (UAV)/Drones, Soil Sensor, Moisture Meters, Rain Gauges, Farm Management Software, Smartphone Applications, and Field Thermometers. The research suggests that the ubiquity of TVs, Radios, and feature phones in rural communities makes these tools the most used devices in farmers' climate-smart practices. However, the level of awareness, availability, accessibility, and utilization of complex tools such as UAVs and simpler tools such as soil sensors, moisture meters, field thermometers, rain gauges, smartphone applications (Facebook, WhatsApp, Twitter, etc.), and farm management software is minimal among rural farmers. The DAS facilitating farmers' climate-smart practices is limited to Digital Agroadvisory Purposes (digital extension), Agri-Digital Finance, and Digital Procurement services, while engagement with other DAS, such as Agri E-Commerce (a typical CSA Institution/Market Smart practice), is non-existing in rural communities. In addition, the Digitalization of Climate-Smart Agriculture, in its present form, is limited to a few CSA practices among smallholders owing to unmet training and information needs for most Climate-Smart Agriculture practices and interventions. Challenges such as DATs' unavailability, inaccessibility, high cost, high (digital)illiteracy, and inadequate extension support for the digitalization of CSA practices limit uptake. The study proposes increased capacity building for smallholders on CSA practice and interventions. Likewise, a strong public-private partnership across multiple scales is needed to stimulate needed investment to enhance farmers' access to affordable, easy-to-use, and tailor-made DATs while recognizing the power dependence and inequalities these digital tools may unleash in rural communities. Finally, increasing sensitization on DAT's use and benefits in rural communities and the larger population is critical to enhancing the widespread Digitalization of Climate-Smart Agriculture practices in smallholding agriculture.
JoinER-BART: Joint Entity and Relation Extraction With Constrained Decoding, Representation Reuse and Fusion

Joint Entity and Relation Extraction (JERE) is an important research direction in Information Extraction (IE). Given the surprising performance with fine-tuning of pre-trained BERT in a wide range of NLP tasks, nowadays most studies for JERE are based on the BERT model. Rather than predicting a simple tag for each word, these approaches are usually forced to design complex tagging schemes, as they may have to extract entity-relation pairs which may overlap with others from the same sequence of word representations in a sentence. Recently, sequence-to-sequence (seq2seq) pre-trained BART models show better performance than BERT models in many NLP tasks. Importantly, a seq2seq BART model can simply generate sequences of (many) entity-relation triplets with its decoder, rather than just tag input words. In this paper, we present a new generative JERE framework based on pre-trained BART. Different from the basic seq2seq BART architecture: 1) our framework employs a constrained classifier which only predicts either a token of the input sentence or a relation in each decoding step, and 2) we reuse representations from the pre-trained BART encoder in the classifier instead of a newly trained weight matrix, as this better utilizes the knowledge of the pre-trained model and context-aware representations for classification, and empirically leads to better performance. In our experiments on the widely studied NYT and WebNLG datasets, we show that our approach outperforms previous studies and establishes a new state-of-the-art (92.91 and 91.37 F1 respectively in exact match evaluation).
Introducing a more-than-quantitative approach to explore emerging structures of feeling in the everyday

• Explores ways of investigating an evolving structure of feeling in a device centric, datafied, more-than-real world. • Includes algorithms and blackbox artificial intelligence models as co-researchers in its more-than-quantitative approach. • Involves a conversation between two Geographers (one with a background in Computer Science, the other in qualitative methods). • Shows interdisciplinary work in progress, putting into practice opportunities and challenges of social media data analysis.
Different photovoltaic power potential variations in East and West China

Photovoltaic (PV) technology can help reduce carbon emissions significantly, but its benefits may be affected by climate change. Few studies have reported on the impact of climate change on the spatial and temporal distribution of solar energy in China based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models, and few have explored the rooftop PV potential. In this study, 16 CMIP6 models are used for evaluation of the inter-annual and seasonal changes of solar irradiance and PV capacity factor under low (SSP1–2.6), medium (SSP2–4.5) and high (SSP5–8.5) greenhouse gas emission scenarios. The effects on the variation of solar irradiance of three factors are examined: clouds, aerosol optical thickness (AOT) and specific humidity. Furthermore, the future power generation potential of rooftop PV is investigated. It is found that the solar irradiance exhibits an upward trend on national level under all the future scenarios, especially for the SSP1–2.6 scenario, with an increasing rate of 1.4 W m−2 decade−1, mainly due to a reduction in AOT. Solar irradiance increases notably in southeast China while increases little in west China, and the level for 2051–2060 remains lower than that for 1960–2014. The PV capacity factor increases in southeast China with increasing solar irradiance, with a maximum increase of about 4% compared to the average PV CF for 1960–2014 and the highest increasing rate being 0.37% decade−1. In addition, to achieve the projected national distributed PV power generation level, >70% of the effective rooftop area needs to be utilized in 2050. This study hopes to enhance understanding of the impact of climate change on solar energy and provide recommendations for future PV planning to better achieve the long-term planetary temperature goal set by the Paris Agreement.
Augmenting Industrial Chatbots in Energy Systems using ChatGPT Generative AI

Chatbots, the automation of communicative labor, have been widely deployed in industrial applications and systems. Built upon the Generative Pre-trained Transformer 3 (GPT-3), ChatGPT is a Generative Artificial Intelligence (AI) primed to transform all pre-existing chatbot capabilities with human-like conversation skills. It has already disrupted many disciplines including tertiary education and academic research methods, with increasing adoption in simple to complex tasks. However, the augmentation of pre-existing industrial chatbots with generative AI capabilities has not been fully investigated and demonstrated in recent literature. In this paper, we address this gap by presenting the augmentation of a pre-existing chatbot using ChatGPT generative AI capabilities. Our contribution encompasses the ten primary human-like conversation capabilities of ChatGPT, its augmentation of the pre-existing functionalities and the adopted prompt engineering strategies. Each capability is empirically demonstrated on Cooee, a functionally deployed chatbot in the microgrid energy systems of the La Trobe Energy Analytics Platform (LEAP).
Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.
Shaping the future of Ethereum: exploring energy consumption in Proof-of-Work and Proof-of-Stake consensus

Ethereum (ETH) is a popular Layer-1 blockchain platform that has been used to create decentralized applications (dApps) and smart contracts. Ethereum 2.0, or Serenity, is a significant update to the network that intends to address numerous issues with scalability, security, and energy efficiency. The Proof-of-Stake (PoS) consensus method will replace the Proof-of-Work (PoW) mechanism, which is one of the major new features of Ethereum 2.0. Given that PoS doesn’t require miners to do intensive mathematical calculations in order to validate transactions, it has the potential to be more energy-efficient than PoW. Additionally, this Ethereum upgrade will also be more secure due to the introduction of a new mechanism called “Casper” that will ensure that validators are always in agreement on the state of the blockchain. The paper begins by discussing the current issues facing Ethereum, including the limitations of the Proof of Work (PoW) consensus mechanism and the need for more efficient and scalable solutions. In this study, we peered at the major changes introduced by Ethereum 2.0, such as the new consensus method (Proof-of-Stake) and the addition of shard chains (Ethereum 2.0), as well as the associated development timelines, benefits and the community criticism on this upgrade.
Research on an Improved Detection Algorithm Based on YOLOv5s for Power Line Self-Exploding Insulators

In the process of inspecting the self-exploding defects of power line insulators, traditional algorithms suffer from various issues such as long detection time, insufficient accuracy, and difficulties in effective detection under complex environments. To address these problems, we introduce an advanced one-stage object detection algorithm called YOLOv5s, which offers fast training and excellent detection performance. In this paper, we applied the YOLOv5s algorithm to improve the detection precision and classification accuracy of insulator self-explosions. To further enhance the YOLOv5s algorithm, we introduced a BiFPN (Bidirectional Feature Pyramid Network) module for feature fusion. This module improved the feature fusion process by learning the importance weights of different input features, considering their contributions. To tackle the challenge of detecting small objects in the self-exploding insulator dataset, we incorporated an SPD (spatial-to-depth convolution) module that focuses on capturing features in small regions and utilizes one-step convolution layers to avoid losing fine-grained information. To address the issue of high similarity between self-exploding insulator regions and intact insulator regions, we introduced an attention mechanism that concentrates attention on the defective insulator regions to gather more information about insulator defects. Experimental results validate that all three improvement methods significantly enhance detection precision. The final model achieves improvements of 2.0% in precision, 0.9% in recall, and 1.5% in average detection accuracy. Through target detection of the test dataset, insulators with self-explosion cases can be effectively detected.
A Survey on Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) is a computational method for finding optimal solutions in a random search space. PSO is a heuristic global optimization method, suggested by Kennedy with Eberhart in the year of 1995. The suggested swarm algorithm (PSO) is inspired from the behavior of swarms living in self-organized groups which fly to search for food sources. In the present time, PSO approach is the most commonly used optimization technique due to its simplicity and efficient performance. The mathematical model of the algorithm uses the velocity, position and fitness of each solution. By using the velocity equation, the group of particles moves in search space. Fitness evaluation function evaluates optimality of the solution. For this reason, researchers are continuously improving the capability of velocity and position counting methods to improve the performance of PSO algorithms. There are lots of methods to navigate the algorithm in the proper search direction by tuning position and velocity equation parameters. This survey paper presents a comprehensive and systematic study on the most basic as well as some of the very recent variants of particle swarm optimization algorithms like classical PSO (CPSO), PSO with time varying acceleration coefficient (PSO-TVAC) and many more approaches. The paper includes a tabular comparison of the various PSO variants and this would be beneficial to the researchers, who are working in the field of swarm intelligence.
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

Quantum processing units (QPUs) are currently exclusively available from cloud vendors.However, with recent advancements, hosting QPUs is soon possible everywhere.Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources.Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum.We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs.We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum.Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity.We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum.To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality.Specifically, we implement a split neural network with optional hybrid QNN predictors.Our results show that extending classical methods with QNNs is viable and promising for future work.
Accelerating Diffusion Models Via Pre-Segmentation Diffusion Sampling for Medical Image Segmentation

Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM requires many iterative denoising steps to generate segmentations from Gaussian noise, resulting in extremely inefficient inference. To mitigate the issue, we propose a principled acceleration strategy, called pre-segmentation diffusion sampling DDPM (PD-DDPM), which is specially used for medical image segmentation. The key idea is to obtain pre-segmentation results based on a separately trained segmentation network, and construct noise predictions (non-Gaussian distribution) according to the forward diffusion rule. We can then start with noisy predictions and use fewer reverse steps to generate segmentation results. Experiments show that PD-DDPM yields better segmentation results over representative baseline methods even if the number of reverse steps is significantly reduced. Moreover, PD-DDPM is orthogonal to existing advanced segmentation models, which can be combined to further improve the segmentation performance.
Weakly-Supervised Diagnosis and Detection of Breast Cancer Using Deep Multiple Instance Learning

The detection and classification of breast cancer lesions with computer-aided diagnosis systems has seen a huge boost in recent years due to deep learning. However, most works focus on 2D image modalities. Dealing with 3D MRI adds new challenges, such as data insufficiency and lack of local annotations. To handle these issues, this work proposes a new two-stage framework based on deep multiple instance learning, which requires only global labels (weak supervision) and provides: 1) classification of the whole volume and of each slice; and 2) 3D localization of lesions through heatmaps. Results show that the proposed approach achieves performances that are competitive with the state of the art, and a qualitative assessment of the heatmaps illustrates the effectiveness of this approach in finding the malignant lesion in the images.
iQuantum: A Case for Modeling and Simulation of Quantum Computing Environments

Today's quantum computers are primarily accessible through the cloud and potentially shifting to the edge network in the future.With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources.This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing.However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems.To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model hybrid quantum-classical computing environments for prototyping and evaluating system design and scheduling algorithms.This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum.Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.
Ship Detection with Optical Image Based on CA-YOLO v3 Network

This article proposes an improved ship target detection network, CA-YOLO v3 Network, which is based on the YOLO v3 algorithm. The aim is to address the challenge of identifying ship categories and positions in complex sea environments, especially when multiple ships occlude each other. Firstly, the ConvNeXt module is used to replace the Darknet53 network in the backbone network, which solves the problem of insufficient image extraction for multiple ship targets, avoids the loss of feature information and reduces the floating-point operation of the network. Secondly, to fully utilize feature information of different scales, the ASFF module is introduced between the output feature layer and detector to achieve feature scale fusion and size unification, which improves the model detection accuracy while ensuring model lightweight. During training, data enhancement is performed on the dataset at the input side, and the GIOU loss function and 9 new anchors matching the ship type are used for training, enabling faster and more accurate determination of the anchor box size and position. Experimental results show that compared to the original YOLO v3 network, the mAP of the CA-YOLO v3 network increases from 74.76% to 95.12%, achieving a significant improvement of 27.2%.
Monte Carlo Tree Search-based Deep Reinforcement Learning for Flexible Operation &amp; Maintenance Optimization of a Nuclear Power Plant

Nuclear Power Plants (NPPs) are required to operate on a flexible profitable production plan while guaranteeing high safety standards. Deep Reinforcement Learning (DRL) is an effective method to find the most profitable Operation & Maintenance (O&M) strategy to adopt in a complex system. However, profit-driven only DRL neglects safety-related issues. In this paper, we propose a DRL approach to solve Single-Objective Sequential Decision Problems (SOSDPs) and Multi-Objective Sequential Decision Problems (MOSDPs) to find O&M strategies that trade off reliability and profit. The combinatorial problem related with the training of the RL agent to search for the optimal solution is addressed by Monte Carlo Tree Search (MCTS), whose performance is compared with the traditionally adopted Proximal Policy Optimization (PPO) & Imitation Learning (IL). A case study is considered for demonstration.
Towards accurate dense pedestrian detection via occlusion-prediction aware label assignment and hierarchical-NMS

With the development of self-driving technology, pedestrian detection models are becoming more useful and important. Although object detection models based on Convolutional Neural Networks have achieved favorable detection results, due to the occlusion in crowded scenarios, dense pedestrian detection is still a challenging task compared with generic object detection. For a CNN-based object detector, an appropriate label assignment method is significant. In this paper, we propose an effective training sample allocation method called Occlusion-Prediction aware Label Assignment (OPLA) to improve the performance of the detector in crowded scenarios. For those Ground Truths (GTs) without severe occlusion, we directly assign prediction boxes with the best matching quality as the corresponding positive samples. In terms of GTs in heavily crowded areas, we fine-tune the matching quality based on visible ratio to enhance the training sample selection priority of severe occluded GTs. In addition, we reveal the deficiency of currently popular post-processing algorithms, Non-Maximum Suppression (NMS) and Soft-NMS for dense pedestrian detection, and propose Hierarchical-NMS (H-NMS) whose strategy diverges with different Intersection over Union (IoU) levels. Experimental results on Crowdhuman, the largest dense pedestrian detection dataset at present, show our approach optimizes the Average Precision (AP) and Miss Rate (MR) of Fully Convolutional One Stage Object Detection (FCOS) by 4.05% and 5.35% and outperforms many state-of-the-art detectors, respectively. Besides, our method does not bring any increase in calculation during inference.