Reviewerly/exHarmony huggingface dataset:
Dynamics of phase space quasi-probability coherence of one of two moving atoms trapped in a cavity coherent field

This study explores the dynamics of atomic mixedness and phase space quasi-probability information of one of two moving atoms trapped in a cavity filled by a coherent field. The atom-photon interactions are considered with the resonance case through nonlinear intensity-dependent coupling. We investigate the impacts of the atom-photon and atomic motion couplings on the dynamics of the atomic Wehrl entropy's mixedness and phase space Husimi distribution information. By helping Wehrl entropy's mixedness dynamics, the dynamics of acceleration and enhancement of the generation of the atomic phase space quasi-probability coherence are examined, and the Husimi distributions of the generated atomic states of one of the two moving atoms are explored. The results are shown that the atomic Husimi distribution and Wehrl entropy's mixedness are susceptible to an increase in the atom-photon and the atomic motion couplings. Therefore, the atomic phase space Husimi distribution coherence can be improved by increasing the atomic motion and nonlinear atom-photon couplings.
Hierarchical reinforcement learning with adaptive scheduling for robot control

Conventional hierarchical reinforcement learning (HRL) relies on discrete options to represent explicitly distinguishable knowledge, which may lead to severe performance bottlenecks. It is possible to represent richer knowledge through continuous options, but reliable scheduling methods are lacking. To design an available scheduling method for continuous options, in this paper, the hierarchical reinforcement learning with adaptive scheduling (HAS) algorithm is proposed. Its low-level controller learns diverse options, while the high-level controller schedules options to learn solutions. It achieves an adaptive balance between exploration and exploitation during the frequent scheduling of continuous options, maximizing the representation potential of continuous options. It builds on multi-step static scheduling and makes switching decisions according to the relative advantages of the previous and the estimated continuous options, enabling the agent to focus on different behaviors at different phases of the task. The expected t-step distance is applied to demonstrate the superiority of adaptive scheduling in terms of exploration. Furthermore, an interruption incentive based on annealing is proposed to alleviate excessive exploration during the early training phase, accelerating the convergence rate. Finally, we apply HAS to robot control with sparse rewards in continuous spaces, and develop a comprehensive experimental analysis scheme. The experimental results not only demonstrate the high performance and robustness of HAS, but also provide evidence that the adaptive scheduling method has a positive effect both on the representation and option policies.
Application of Deep Learning to the Prediction of Solar Irradiance through Missing Data

The task of predicting solar irradiance is critical in the development of renewable energy sources. This research is aimed at predicting the photovoltaic plant’s irradiance or power and serving as a standard for grid stability. In practical situations, missing data can drastically diminish prediction precision. Meanwhile, it is tough to pick an appropriate imputation approach before modeling because of not knowing the distribution of datasets. Furthermore, not all datasets benefit equally from using the same imputation technique. This research suggests utilizing a recurrent neural network (RNN) equipped with an adaptive neural imputation module (ANIM) to estimate direct solar irradiance when some data is missing. Without imputed information, the typical projects’ imminent 4-hour irradiance depends on gaps in antique climatic and irradiation records. The projected model is evaluated on the widely available information by simulating missing data in each input series. The performance model is assessed alternative imputation techniques under a range of missing rates and input parameters. The outcomes prove that the suggested methods perform better than competing strategies when measured by various criteria. Moreover, combine the methodology with the attentive mechanism and invent that it excels in low-light conditions.
Weakly Annotated Residential Area Segmentation Based on Attention Redistribution and Co-Learning

Residential area segmentation is of great significance in remote sensing field. Training the segmentation network with image-level weakly annotated data has become a research hot spot due to the easy access of classification labels. The quality of class activation maps (CAMs) is crucial for obtaining accurate segmentation results. Limited to irregular shapes, tortuous boundaries and greatly varied scales of targets in remote sensing images, generating high-quality CAMs is still a great challenge. To solve these problems, a novel weakly annotated residential area segmentation model based on attention redistribution and co-learning (ARC) is proposed in this paper. We develop Aggregate-and-Distribute based feature coupling to achieve the redistribution of attention on channel and spatial dimensions, which deals with multi-level features at the same time and makes them fully embedded together. Such arrangement can effectively capture the shape characteristic of targets and filter out complicated background. In order to mitigate the impact of ambiguous regions like surroundings of boundaries and potential scattered houses, a confusion co-learning strategy is designed to jointly explore the class-specific features and refine the cross-class features through a two-stream classifier with sharing weights, which helps generate sharper edges and discover ignored targets. Experimental results on GeoEye-1, SPOT5 and Landsat8 datasets reveal that our proposal outperforms the competing methods by a large margin in both subjective and objective assessments.
Fabrication of Flexible Role-Based Access Control Based on Blockchain for Internet of Things Use Cases

The Internet of Things (IoT) connects many objects and allows continuous communication and data sharing has emerged as a revolutionary technology. However, expanding IoT devices has raised concerns regarding data security and access control. Traditional access control mechanisms face challenges in managing access rights, particularly in scenarios where multiple users with the same roles try to access several resources which may lead to conflicting roles. Additionally, there is also an overhead of system performance using traditional approaches. In existing studies, the main problem of conflict roles is not addressed or not even identified appropriately. This paper proposes a framework to address these challenges using blockchain technology and role-based access control with a smart contract implementation on the hyperledger fabric framework. The proposed methodology introduces a role management system that resolves conflicts based on predefined rules and user preferences. It employs a consensus mechanism to determine access permissions, ensuring fairness and accountability. The findings demonstrate that applying the suggested framework eliminates conflicting problems, improves system security and also provides better results in response times of twelve seconds on average for thirty-five concurrent user requests. It also provides a secure and transparent access control framework which improves response time and provides security based on roles.
Intelligent Fault Diagnostic Model for Industrial Equipment Based on Multimodal Knowledge Graph

Industrial equipment failure diagnosis is a crucial issue that impacts the national industrial manufacturing level, economic cycle development, and sustainable technological advancement. A multimodal knowledge graph (MMKG)-based intelligent diagnostic model for industrial equipment fault is proposed to address the issues of insufficient and inadequate fault data samples encountered when using a single-mode model for fault diagnosis in existing industrial equipment. This model does not require extensive data learning for equipment fault diagnosis in complex industrial scenarios. The model utilizes an improved faster region with CNN (Faster RCNN) features the object detection module to extract visual information feature vectors of semiordered main and nonmain objects. These feature vectors are then mapped to entity, attribute, and relationship vectors in a knowledge graph using cosine similarity for feature correspondence mapping. The semantic matching inference is performed based on this mapping, resulting in a set of fault triplets. Finally, the bidirectional and autoregressive transformers (BARTs) text generation model processes this triplet set to generate fault diagnosis texts. Experimental results demonstrate that the improved Faster RCNN object detection model achieves a 1.2% increase in confidence when trained with small training datasets. The accuracy of generated fault description texts reaches approximately 98% compared to standard texts. The model presented in this article addresses the challenge of diagnosing faults in industrial equipment, particularly in complex scenarios with limited data, such as substations. It enhances the target detection model to effectively extract visual features even when data is scarce. Additionally, it utilizes an MMKG to enable interpretable intelligent decision-making.
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality

The object inference for augmented reality (AR) requires a precise object localization within user's physical environment and the adaptability to dynamic communication conditions. Deep learning (DL) is advantageous in capturing highly-nonlinear features of diverse data sources drawn from complex objects. However, the existing DL techniques may have disfluency or instability issues when deployed on resource-constrained devices with poor communication conditions, resulting in bad user experiences. This paper addresses these issues by proposing a deep adaptive inference network called DeepAdaIn-Net for the real-time device-edge collaborative object inference, aiming at reducing feature transmission volume while ensuring high feature-fitting accuracy during inference. Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module. The PPS module minimizes the total execution latency, including inference and transmission latency. The HFCL and BaFC modules can decouple the training and inference process by integrating a high-compression ratio feature encoder with the bandwidth-aware feature configuration, which ensures that the compressed data can adapt to the varying communication bandwidths. The FCC module fills the information gaps among the compressed features, guaranteeing high feature expression ability. We conduct extensive experiments to validate DeepAdaIn-Net using two object inference datasets: COCO2017 and emergency fire datasets, and the results demonstrate that our approach outperforms several conventional methods by deriving an optimal 123x feature compression for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$640\times 640$</tex-math></inline-formula> images, which results in a mere 63.3 ms total latency and an accuracy loss of less than 3% when operating at a bandwidth of 16 Mbps.
On-Device Tiny Machine Learning for Anomaly Detection Based on the Extreme Values Theory

The significance of anomaly detection is particularly pronounced in Industry 4.0 applications. For instance, in manufacturing, the timely detection of equipment malfunctions can prevent costly downtime and maintain production efficiency. In energy systems, spotting anomalies in power consumption patterns can enhance resource allocation and optimize energy usage. Equally noteworthy is the ascendancy of tiny machine learning (TinyML), emerging as a potent tool for real-time anomaly detection, exemplifying its versatile utility. This work presents an unsupervised on-device learning TinyML algorithm, drawing inspiration from the extreme value theory. The algorithm leverages the two-parameter Weibull distribution function to efficiently identify anomalies within discrete time series data. Optimal hyperparameters are ascertained via grid search methodology. Notably, employing synthetic data with randomized anomalies elucidates the algorithm’s proficiency in binary classification within time series, highlighting an accuracy of 99.80%, recall of 93.10%, and F1 score of 96.43%. The amalgamation of theoretical foundations from the extreme value theory and practical capabilities of TinyML accentuates its pertinence across a broad spectrum of domains.
TEFNEN: Transformer for Energy Forecasting in Natural Environment

Photovoltaic systems are being used in almost every field such as smart cities, Internet of Things paradigms or remote Wireless Sensor Networks. In Internet of things paradigms deployed in natural environments, energy harvesting technology is crucial to power the devices. For the energy management system, it is important to predict how much energy can be harvested from the environment. In this work we focus on creating a model for forecasting the total energy produced by a photovoltaic installation one day in advance. The model is based on the original Transformer architecture. This structure has minor modifications for time series applications. The dataset was created with weather forecasts and the energy and power production of a real photovoltaic installation. The model was trained and compared with state-of-art approaches. The results show that our approach could predict the total energy generated by the photovoltaic installation one day-ahead.
Architecture Design &amp; System Sizing of a Blockchain-based National Population Census

The immutable, decentralized, smart contract-enabled, time-stamped, shareable, and append-only characteristics of blockchain technology have made it a subject of interest in almost every industry. The government sector is also exploring its potential uses to foster transparency, trust, and accountability. However, conducting a population census that is completely trustworthy, transparent, and accountable is a challenge, and blockchain technology could be used to address some of these issues. The current census system has shortcomings, such as incomplete population coverage and racial or ethnic discrimination. Our proposal involves the use of blockchain technology to create a solution for conducting a national population census in Pakistan, which would help alleviate these issues. In this paper, we investigate the architecture design and system capacity required for this blockchain-based solution. Our insights and findings will help advance the field of blockchain and promote the development of new and innovative solutions for census.
A Hybrid Approach for Smart Alert Generation

Anomaly detection is an important task in network management. However, deploying intelligent alert systems in real-world large-scale networking systems is challenging when we take into account (i) scalability, (ii) data heterogeneity, and (iii) generalizability and maintainability. In this paper, we propose a hybrid model for an alert system that combines statistical models with a whitelist mechanism to tackle these challenges and reduce false positive alerts. The statistical models take advantage of a large database to detect anomalies in time-series data, while the whitelist filters out persistently alerted nodes to further reduce false positives. Our model is validated using qualitative data from customer support cases. Future work includes more feature engineering and input data, as well as including human feedback in the model development process.
Leveraging Mixed-Precision CNN Inference for Increased Robustness and Energy Efficiency

Convolutional Neural Networks (CNNs) show tremendous performance in many Computer Vision (CV) tasks like image segmentation crucial to autonomous driving. However, they are computationally demanding and usually not robust to image corruptions like weather influences. In this paper, we introduce our mixed-precision inference method to overcome these two challenges. Therefore, we enable mixed-precision CNN execution on modern embedded system on chips (SoC) that feature a DNN accelerator and a reconfigurable fabric. In case of a weather change, we can quickly adjust the inference precision to maintain model accuracy, while benefitting from fewer off-chip memory accesses compared to full precision. Therefore, we identify optimal quantization schemes for different weather conditions that maximize model accuracy and minimize data offloading. To enable mixed-precision inference, we present our dynamic number conversion architecture for data going back and forth to the off-chip memory, hosted on the reconfigurable tile of the SoC. Using a DeepLabV3+ model with a Resnet-101 backbone for image segmentation, for example, our evaluation yields 60% less off-chip movements under clear weather conditions. Applying rain, fog, and brightness to the input of various models, we can report an up to 26%, 23.8% and 45% reduction in data transactions, respectively, while maintaining the baseline model accuracy. We finally demonstrate that our architecture does not impact the throughput of the CNN inference and consumes very few resources of the reconfigurable fabric.
On the Viability of Using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators

Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to stringent power and computational budgets. An effective solution to this issue is software-hardware (SW-HW) co-design, which allows for the tailored creation of DNN models and hardware architectures that optimally utilize available resources. However, SW-HW co-design traditionally suffers from slow optimization speeds because their optimizers do not make use of heuristic knowledge, also known as the "cold start" problem. In this study, we present a novel approach that leverages Large Language Models (LLMs) to address this issue. By utilizing the abundant knowledge of pre-trained LLMs in the co-design optimization process, we effectively bypass the cold start problem, substantially accelerating the design process. The proposed method achieves a significant speedup of 25x. This advancement paves the way for the rapid and efficient deployment of DNNs on edge devices.
Optimal Cruise Airspeed for Hybrid-Electric and Electric Aircraft: Applications to Air Mobility

Electric and hybrid-electric aircraft can help our society transition towards more sustainable aviation and lower greenhouse gas (GHG) emissions. This paper finds the speed that minimizes the direct operating cost (DOC) for hybrid-electric aircraft. The solution is the positive real root of a quintic polynomial which is derived using Pontryagin’s minimum principle. By properly selecting a hybridization factor, one can also find the cruise airspeed corresponding to the minimum DOC of an electric aircraft. The optimal airspeed is integrated into the Rapidly-exploring Random Trees Star (RRT*) path planning algorithm. The minimum DOC solutions are investigated in a hybrid-electric international travel scenario and the path planning approach is applied to an electric aircraft city scenario.
Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning

Abstract: The objective of mobile robot path planning (MRPP) is to devise the shortest obstacle-free path for autonomous mobile robots based on a given terrain. Numerous MRPP methods have been extensively researched. This paper presents a novel approach called Opposition-based Learning Equilibrium Optimizer (OEO) for generating smooth paths for mobile robots. The fundamental idea behind OEO is to introduce an opposition-based learning mechanism while maintaining the overall framework of the basic EO algorithm. This modification alleviates the susceptibility of the basic EO algorithm to local optima. The OEO algorithm is employed to provide smooth paths for autonomous mobile robots, and the results are compared with several classical metaheuristic algorithms. Comparative analysis across different environments demonstrates that the proposed OEO-based path planning method consistently yields the shortest and most collision-free paths with superior stability.
Backdoor attack and defense in federated generative adversarial network-based medical image synthesis

Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Training generative adversarial neural networks (GANs) usually require large amounts of training data. Federated learning (FL) provides a way of training a central model using distributed data while keeping raw data locally. However, given that the FL server cannot access the raw data, it is vulnerable to backdoor attacks, an adversarial by poisoning training data. Most backdoor attack strategies focus on classification models and centralized domains. It is still an open question if the existing backdoor attacks can affect GAN training and, if so, how to defend against the attack in the FL setting. In this work, we investigate the overlooked issue of backdoor attacks in federated GANs (FedGANs). The success of this attack is subsequently determined to be the result of some local discriminators overfitting the poisoned data and corrupting the local GAN equilibrium, which then further contaminates other clients when averaging the generator's parameters and yields high generator loss. Therefore, we proposed FedDetect, an efficient and effective way of defending against the backdoor attack in the FL setting, which allows the server to detect the client's adversarial behavior based on their losses and block the malicious clients. Our extensive experiments on two medical datasets with different modalities demonstrate the backdoor attack on FedGANs can result in synthetic images with low fidelity. After detecting and suppressing the detected malicious clients using the proposed defense strategy, we show that FedGANs can synthesize high-quality medical datasets (with labels) for data augmentation to improve classification models' performance.
Postdigital Visual Arts

Postdigital visual arts describe the controversial condition of arts and its relationships with media after the emergence of digital technologies. The term involves artistic practice in the postdigital age and the artistic study of interactions between technology and social/cultural life. In the early 2000s, postdigital visual arts were understood as a critical reflection of digital aesthetic immaterialism. Towards mid-2020s, the concept has grown much wider and now includes blurred disciplinary boundaries, material and creative hybridity.
Postdigital Curriculum

Curriculum is derived from the Latin currere, often translated as ‘the course of a race’. Applied to education, the term is typically used to describe a statement (in the form of a printed or digital document) of the course of study undertaken by a learner. Curricula exist at a national level to direct primary and secondary education, university degree programmes, individual courses, and professional programmes of study. Despite its centrality to education and its everyday usage, the concept can be depicted as ‘ungraspable’. This entry describes the different understandings of curriculum and the development of a postdigital approach to it.