Reviewerly/exHarmony huggingface dataset:
A TSP-Based Online Algorithm for Multi-Task Multi-Agent Pickup and Delivery

The Multi-Agent Path Finding (MAPF) and its extension, Multi-Agent Pickup and Delivery (MAPD), have received much attention in academia. In industry, on the other hand, automatic control of teams of robots and AGVs on factory floors and logistic warehouses for pickup and delivery operations have also been studied intensively. Currently, MAPD problem formulation does not fully capture important aspects of many real-world industrial applications, e.g., MAPD allocates only one task at a time for each agent, payload capacity for each agent is ignored, and pickup & dropoff operations are assumed to be done immediately. In this letter, we extend MAPD problem to a multi-task setting where each agent is allocated multiple tasks considering payload capacity as well as pickup & dropoff cost. We propose an online multi-task MAPD algorithm which is a combination of MAPF and Traveling Salesman Problem (TSP) algorithm. Comparisons between the proposed and conventional MAPD show that the proposed MAPD is able to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$18\% -38\%$</tex-math></inline-formula> shorter makespan paths in wide range of agent numbers. We also examine the behavior of the proposed online multi-task MAPD by changing payload capacity distribution and pickup & dropoff cost. Simulation results indicate that increase of pickup cost can largely increase the makespan when agent number is small; on the other hand, increase of dropoff cost tend to increase the makespan when agent number is large. Our empirical study also demonstrates that the proposed online multi-task MAPD is applicable to large scale environment (e.g., agent number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$=300$</tex-math></inline-formula> ) in an online manner.
Mixup Feature: A Pretext Task Self-Supervised Learning Method for Enhanced Visual Feature Learning

Self-supervised learning has emerged as an increasingly popular research topic within the field of computer vision. In this study, we propose a novel self-supervised learning approach based on Mixup features as pretext tasks. The proposed method aims to learn visual representations by predicting the Mixup-Feature of a masked image, which serves as a proxy for higher-level semantic information. Specifically, we investigate the efficacy of Mixup features as the prediction target for self-supervised learning. By setting the hyperparameter λ through Mixup operations, pairwise combinations of Sobel edge feature maps, HOG feature maps, and LBP feature maps are created. We employ the vision transformer as the backbone network, drawing inspiration from masked autoencoders (MAE). We evaluate the proposed method on three benchmark datasets, namely Cifar-10, Cifar-100, and STL-10, and compare it with other state-of-the-art self-supervised learning approaches. The experiments demonstrate that mixed HOG-Sobel feature maps after Mixup achieve the best results in fine-tuning experiments on Cifar-10 and STL-10. Furthermore, compared to contrastive learning-based self-supervised learning methods, our approach proves to be more efficient, with shorter training durations and no reliance on data augmentation. When compared to generative self-supervised learning approaches based on MAE, the average performance improvement is 0.4%. Overall, the proposed self-supervised learning method based on Mixup features offers a promising direction for future research in the computer vision domain and has the potential to enhance performance across various downstream tasks. Our code will be published in GitHub.
Information Communication Technology (ICT) and Its Effects on Social and Political Inclusion in Africa

Available statistics show that the overall social and political inclusion remains at startlingly low levels in many societies. However, it is believed that information communication technologies (ICT) can provide veritable tools for the promotion of social and political inclusion. This study, therefore, investigates the effects of ICT on social and political inclusion, using a system generalized method of moments for the case of Africa for the period 1995–2018. The empirical results show that ICT has a positive statistically significant effect on social and political inclusion, indicating that the higher the levels of ICT, the higher the levels of social and political inclusion. Whereas most of the studies in the literature have suggested a relationship between ICT and social inclusion, on one hand, and IC and political inclusion, on the other, this study has pushed the envelope and expanded the literature by confirming empirically the significant positive effect of ICT on social and political inclusion. Along these lines, the study shows that ICT plays an important role in social and political inclusion. ICT, therefore, holds much potential to foster social and political inclusion. It is recommended that the government of individual countries in Africa should increase efforts in fostering robust economies and sound institutions needed for the absolute implementation of ICT, to take advantage of all the benefits required for social and political inclusion.
A Generic Interactive Membership Function for Categorization of Quantities

The membership function is to categorize quantities along with a confidence degree. This article investigates a generic user interaction based on this function for categorizing various types of quantities without modification, which empowers users to articulate uncertainty categorization and enhance their visual data analysis significantly. We present the technique design and an online prototype, supplementing with insights from three case studies that highlight the technique's efficacy among different types of quantities. Furthermore, we conduct a formal user study to scrutinize the process and reasoning users employ while utilizing our technique. The findings indicate that our technique can help users create customized categories. Both our code and the interactive prototype are made available as open-source resources, intended for application across varied domains as a generic tool.
Design of improved PBFT algorithm based on aggregate signature and node reputation

The alliance chain system is a distributed ledger system based on blockchain technology, which can realize data sharing and collaboration among multiple parties while ensuring data security and reliability. The Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is the most popular consensus protocol in the alliance chain, but the algorithm has problems such as high complexity and too simple election of the master node, which will make PBFT unable to be applied in scenarios with too many nodes. At the same time, there are certain security issues. In order to solve these problems, this paper proposes an improved Byzantine consensus algorithm, Polymerization Signature and Reputation Value PBFT (P-V PBFT). Firstly, the consistency protocol process is improved based on the aggregate signature technology. The simulation results show that the P-V PBFT algorithm can effectively reduce the overhead of network transmission, and the time complexity of the algorithm decreases exponentially, which improves the efficiency of the consensus process. Secondly, the node reputation election mechanism is introduced to elect the primary node, and the security analysis is carried out to verify the fairness and security of the primary node election of the P-V PBFT algorithm. Therefore, as a feasible improvement of the blockchain consensus protocol, the P-V PBFT algorithm can provide more efficient and secure guarantee for the blockchain system in practical application.
Short-term Forecasting for Load and Solar Generation in Electrical Distribution Networks

The nonlinear nature, the huge volume of information, and the uncertainty of data, are causing many challenges to the accuracy of load and renewable forecasting. In this paper, a load-forecasting model based on an artificial neural network (ANN) with back-propagation learning algorithm is developed. A significant volume of real historical data on load consumption, weather conditions, and types of the day was considered in the proposed load forecasting method for training and assessing. Later, a forecasting solution in pseudo-real-time for renewable generation is given. For this, we present the application for solar production forecasting techniques developed by CEA, with the inputs being images from meteorological forecasts and satellite or sky-imager data. The evaluation results derived from the real demonstration at the SOREA grid in France have shown that both proposed forecasting methods could achieve high accuracy in forecasting load consumption and solar generation.
High-Voltage Insulator Surface Pollution Classification Using Insulator Type-Specific CNNs

Outdoor high voltage (HV) insulators are essential components for entire power grids. They are utilized to provide mechanical structural support and isolate the grounded towers from the live conductors. Insulators are exposed to numerous environmental pollutions such as soluble and insoluble contaminations, along with natural pollutions like dust and snow that could lead to poor performance or malfunction of such insulators. To address this issue, deep learning-based image processing tools can be used to detect pollutants efficiently and accurately from insulator surface images. This approach generally follows a common methodology: 1) collect images for various types of insulators and under different surface conditions; and 2) train a convolutional neural network (CNN) to classify images-and this training is without any consideration being made on the insulator type. Fitting one CNN for all insulator types could be problematic since the appearance of an insulator generally depends on its type and the appearance of the pollutants on its surface thereof. Subsequently, in the process of data collection, some types of insulators could be underrepresented (i.e., data being imbalanced with respect to the insulator type), and this, in turn, could lead to inaccurate classification for those insulators. In contrast with this common approach, in this study, we show that by stratifying the data based on the insulator materials type, which is generally known in advance, and then training insulator-specific CNNs for surface condition classification, one can considerably boost the classification performance. In particular, our empirical results show that the training insulator-specific CNNs lead to an 8-10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> improvement in classification accuracy when compared with a single CNN that is trained for all insulator types.
Model-Based Reinforcement Learning with State Abstraction: A Survey

Model-based reinforcement learning methods are promising since they can increase sample efficiency while simultaneously improving generalizability. Learning can also be made more efficient through state abstraction, which delivers more compact models. Model-based reinforcement learning methods have been combined with learning abstract models to profit from both effects. We consider a wide range of state abstractions that have been covered in the literature, from straightforward state aggregation to deep learned representations, and sketch challenges that arise when combining model-based reinforcement learning with abstraction. We further show how various methods deal with these challenges and point to open questions and opportunities for further research.
A TEMPEST vulnerability prediction method for cyber security practitioners

Sensitive information can have its security compromised by unintentional electromagnetic emissions from the information technology equipment (ITE) being used to process it. It is important to assess the likelihood of a potential compromise, and this requires radio frequency (RF) engineering expertise to predict the likelihood of the vulnerability occurring. This paper describes the development of a fuzzy inference system that can be used to assess the radiated and conducted vulnerability likelihood of unintentional electromagnetic emanations. The system has the potential to be a valuable tool for cybersecurity practitioners without RF expertise. The system has been tested on office-based ITE devices, and it is effective in predicting the likelihood of radiated and conducted vulnerabilities occurring. Areas of future work include extending the fuzzy inference system to use RF propagation models and enabling it to make vulnerability likelihood predictions after countermeasures have been applied.
RADENN: A Domain-Specific Language for the Rapid Development of Neural Networks

RADENN is a domain-specific language designed to rapidly develop fully connected neural networks for classification and regression problems. The primary objective of this language is to make neural network algorithms more accessible to a broader audience. RADENN is built on top of Keras API with Tensorflow as its back-end. This language follows the imperative paradigm; it uses dynamic scoping, is weakly typed, and utilizes type inference. The contribution of RADENN is to incorporate specific data types and built-in functions to facilitate the creation, training, and evaluation of neural networks. All these features make RADENN an ideal tool for Data Scientists, Data Analysts, Big Data Engineers, Software Enginers, and anyone who needs a fast and efficient way to create prototypes and models without extensive programming or deep learning knowledge. This work provides a detailed overview of the features of RADENN and compares it to Keras and PyTorch, which are currently among the most widely used libraries in industry and research.
Weber vector local pattern

Feature extraction of face images, especially those with significant illumination, occlusion and facial expression changes, is a hot topic in the field of computer vision. This paper presents a novel local image descriptor called Weber vector local pattern (WVLP). WVLP uses the salient variations within an image to simulate the pattern perception of human beings. Different from most WLD-like methods which describe a pixel with the isolated salient variations and direction components, WVLP introduces the Weber vector to deduce two compound features, i.e., the salient variations and the direction feature, which work together to make the feature images more discriminative. During the first feature transformation, two sizes of neighborhoods are used to capture the structural features, and four feature images were output. After that, an LBP-like trick, named neighbors to center difference binary pattern (NCDBP) is used to further extract the high-level complex structure information in a large range of neighborhood. Experimental results on the several face recognition databases show that WVLP outperforms other compared descriptors, even in the database with challenges of illumination, facial expression, and occlusion changes.
20.2: <i>Invited Paper:</i> Fast and Robust High Precision Luminance Image Stitching in Uniformity and DeMURA measurements

This paper provides an overview of image stitching and its general advantages and challenges. Further, we introduce a novel stitching concept based on our advanced pixel registration (APR) procedure. It allows easy and comparable flexible stitching setups for DeMURA and uniformity measurements in laboratory and production environments.
P‐4.3: An Adaptive Foveal Image Transmission Algorithm Based on Human Eye Gaze Point

With the development of display devices towards high resolution and high refresh rate, the amount of video image data is increasing exponentially, which brings challenges to the bandwidth resources of the display system. From the perspective of human visual system, combining the change of the gaze point position and the sampling characteristics of the human eye foveal, an adaptive foveal image transmission algorithm based on human eye gaze point is proposed to reduce the data transmission bandwidth. The experimental results show when the viewing distance is 2 and the gaze point position is at the center of the image. The comprehensive compression ratio is 12.257, the data transmission clock frequency is 12.532MHz, and the total amount of data transmitted is 8.158% of the original total. The total amount of data transmission is greatly reduced, and the pressure on data transmission bandwidth is effectively relieved. So that video images with higher resolution and refresh rate can be displayed under the same clock frequency.
FMSA: a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

Abstract Intrusion detection systems are increasingly using machine learning. While machine learning has shown excellent performance in identifying malicious traffic, it may increase the risk of privacy leakage. This paper focuses on implementing a model stealing attack on intrusion detection systems. Existing model stealing attacks are hard to implement in practical network environments, as they either need private data of the victim dataset or frequent access to the victim model. In this paper, we propose a novel solution called Fast Model Stealing Attack (FMSA) to address the problem in the field of model stealing attacks. We also highlight the risks of using ML-NIDS in network security. First, meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state. Then, the number of accesses to the target model is used as an optimization term, resulting in minimal queries to achieve model stealing. Finally, adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data. Through experiments on multiple public datasets, compared to existing state-of-the-art algorithms, FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9% and the similarity with the target model to 90.1%. We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.
P‐4.11: Image Deraining using Multi‐scale Generative Adversarial Network

The images obtained for automatic driving and security monitoring are easily affected by rain, which will bring serious difficulties to subsequent image‐processing tasks. To tackle the problem, deraining algorithms are widely adopted. However, most of the traditional methods fail to restore rain images when raindrops come in different sizes and densities. In this paper, a single‐image deraining algorithm based on multi‐scale generative adversarial network (MS‐GAN) is proposed. This algorithm uses three‐level Laplace pyramid to decompose the input rain image into different scales, inputs them into three modified residual networks and uses adversarial network to improve the effect of learning. Experimental results show that the proposed algorithm can remove raindrops more efficiently and reduce loss of detail, even when raindrops is of different sizes.
Quaternionic quantum Turing machines

Quaternionic quantum theory is an extension of the standard complex quantum theory. Inspired by this, we study the quaternionic quantum computation using quaternions. We first develop a theory of quaternionic quantum Turing machines as a model of quaternionic quantum computation. Quaternionic quantum Turing machines can also be seen as a generalization of the complex quantum Turing machine. Then, we introduce the weighted sum of quaternionic quantum Turing machines and establish some of their basic properties.
Migrant remittances, financial market development, and per capita real growth in sub-Saharan Africa

The inflow of migrant remittances to developing economies in the recent past has been one of the most topical issues discussed in growth literature due to the increasing volume, stable nature, and capacity to enhance growth.This study examines the effect of migrant remittances and financial market development, on per capita real growth in sub-Saharan Africa (SSA).Data from twenty-seven African countries between the period 2000 and 2020 was employed for this study.The pool mean group (PMG) was deployed in analyzing the data.The study outcome revealed that migrant remittances positively influence and facilitate growth in the SSA region.The study also affirmed that equity market development contributes positively to growth in the SSA region.Furthermore, the study also established that banking sector development seems not to affect growth positively in the SSA.In addition, the study also recommends some policies for the region to implement.
Spread and asymmetry of typical quantum coherence and their inhibition in response to glassy disorder

Abstract We consider the average quantum coherences of typical redits and qudits—vectors of real and complex Hilbert spaces—with the analytical forms stemming from the symmetry of Haar-uniformly distributed random pure states. We subsequently study the response to disorder in spread of the typical quantum coherence in response to glassy disorder. The disorder is inserted in the state parameters. Even in the absence of disorder, the quantum coherence distributions of redits and qudits are not uniform over the range of quantum coherence, and the spreads are relatively lower for higher dimensions. On insertion of disorder, the spreads decrease. This decrease in the spread of quantum coherence distribution in response to disorder is seen to be a generic feature of typical pure states: we observe the feature for different strengths of disorder and for various types of disorder distributions, viz. Gaussian, uniform, and Cauchy–Lorentz. We also find that the quantum coherence distributions become less asymmetric with increase in dimension and with infusion of glassy disorder.