Reviewerly/exHarmony huggingface dataset:
Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.
Accurate Methods for the Analysis of Strong-Drive Effects in Parametric Gates

The ability to perform fast, high-fidelity entangling gates is an important requirement for a viable quantum processor. In practice, achieving fast gates often comes with the penalty of strong-drive effects that are not captured by the rotating-wave approximation. These effects can be analyzed in simulations of the gate protocol, but those are computationally costly and often hide the physics at play. Here, we show how to efficiently extract gate parameters by directly solving a Floquet eigenproblem using exact numerics and a perturbative analytical approach. As an example application of this toolkit, we study the space of parametric gates generated between two fixed-frequency transmon qubits connected by a parametrically driven coupler. Our analytical treatment, based on time-dependent Schrieffer-Wolff perturbation theory, yields closed-form expressions for gate frequencies and spurious interactions, and is valid for strong drives. From these calculations, we identify optimal regimes of operation for different types of gates including $i$SWAP, controlled-Z, and CNOT. These analytical results are supplemented by numerical Floquet computations from which we directly extract drive-dependent gate parameters. This approach has a considerable computational advantage over full simulations of time evolutions. More generally, our combined analytical and numerical strategy allows us to characterize two-qubit gates involving parametrically driven interactions, and can be applied to gate optimization and cross-talk mitigation such as the cancellation of unwanted ZZ interactions in multi-qubit architectures.
A Classification Method for Network Applications using BP Neural Network

It is necessary to understand the characteristics of network applications for network planning, network management and network security. This paper proposed a method to classify network applications by using BP neural network to parse network trace files. Firstly, six characteristics of network application flow are proposed by analyzing PCAP files. Secondly, based on the BP neural network model, its connection weights and thresholds are obtained through supervised training, and the architecture classification of network applications can be completed by inputting the PCAP datasets. We tested the proposed method on the PCAP datasets obtained from CERNET, and analyzed the influence of the network application flow characteristics on the BP neural network model. The experimental results show that the proposed method is a simple and practical method for network application classification.
An Exploratory Analysis of Effect of Adversarial Machine Learning Attack on IoT-enabled Industrial Control Systems

Machine Learning (ML)-based Intrusion Detection Systems (IDS) is an effective technology to automatically detect cyber attacks in the Internet of Things (IoT) dependent Industrial Control Systems (ICS). It is faster, more efficient, and can detect attacks without human intervention. However, ML-based IDSs have introduced another security threat called Adversarial Machine Learning (AML). An AML attack may cause severe industrial infrastructural and production damage resulting in substantial financial loss. This paper presents an exploratory analysis of initiating an AML attack using adversarial samples created using a Fast Gradient Sign Method (FGSM). The research presented in this paper has been conducted from a dataset generated from a full-fledged singular module of a power distribution industry controlled by IoT-enabled ICSs. We explored the AML attack on Gradient Boosting (GB) and Iterative Dichotomiser 3 (ID3) model and discovered the average classification accuracy, precision, recall, and F1-scores are 87%, 88%, 87.5%, and 87%, respectively. The AML attack reduces the average precision, recall, and F1-score by 20.5%, 20.5%, and 22.5%, respectively, when 50% perturbations are added to 10% samples.
Levy Flight-Based Improved Grey Wolf Optimization: A Solution for Various Engineering Problems

Optimization is a broad field for researchers to develop new algorithms for solving various types of problems. There are various popular techniques being worked on for improvement. Grey wolf optimization (GWO) is one such algorithm because it is efficient, simple to use, and easy to implement. However, GWO has several drawbacks as it is stuck in local optima, has a low convergence rate, and has poor exploration. Several attempts have been made recently to overcome these drawbacks. This paper discusses some strategies that can be applied to GWO to overcome its drawbacks. This article proposes a novel algorithm to enhance the convergence rate, which was poor in GWO, and it is also compared with the other optimization algorithms. GWO also has the limitation of becoming stuck in local optima when used in complex functions or in a large search space, so these issues are further addressed. The most remarkable factor is that GWO purely depends on the initialization constraints such as population size and wolf initial positions. This study demonstrates the improved position of the wolf by applying strategies with the same population size. As a result, this novel algorithm has enhanced its exploration capability compared to other algorithms presented, and statistical results are also presented to demonstrate its superiority.
A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

E-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher’s perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company’s profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners’ standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses.
Towards inclusive growth in Africa: Remittances, and financial development interactive effects and thresholds

The study employs macro data for 42 African countries to examine the interactive and threshold effects of financial development in the remittances-inclusive growth relationship. First, evidence based on the system GMM estimator shows that remittances are not statistically significant in promoting inclusive growth in Africa. Notably, across the economic growth and income inequality dimensions of inclusive growth, we find that although remittances are ineffective in boosting the former, they deepen the latter. Second, we find that Africa’s underdeveloped financial sector dampens the marginal positive effect of remittances on inclusive growth. Third, our threshold analysis indicates that for financial development to interact with complementary policies to foster inclusive growth in Africa, a minimum threshold of 14.5% is required. We conclude by informing policy on the level of investments needed for financial development to promote fairer income growth and distribution in Africa.
Class-incremental continual learning model for plunger pump faults based on weight space meta-representation

Deep learning (DL)-based fault diagnosis models often require full access authority of all fault modes, which are unrealistic. In industrial scenarios, the type of fault database samples is gradually increasing, requiring that the diagnostic model is updated repeatedly. However, DL models inevitably suffer from catastrophic forgetting during continual learning. To accommodate dynamic changes in fault types, we propose a continual learning model based on weight space meta-representation (WSMR) applied to class-incremental fault diagnosis of switch machine plunger pumps. The proposed Modified WaveletKernelNet (MWKN) demonstrates that improving base model structure can reduce forgetting of old knowledge. WSMR maintains the diagnostic performance of the model for both new and old faults in a cost-friendly manner by inferring about and preserving the meta-representation distribution of the task-specific base model. The results show that WSMR with MWKN effectively alleviates catastrophic forgetting and outperform other methods.
Large group decision-making based on interval rough integrated cloud model

Since different uncertainties exist in the large group decision-making (LGDM) process, such as randomness, diversity and fuzziness, a single method may be insufficient to address LGDM. Hence, this paper proposes a hybrid model that a new similarity calculation method for cloud model, the netting clustering and interval rough integrated cloud (IRIC) are combined to solve LGDM in uncertain linguistic environment. First, a new similarity method for cloud model is presented, based on which a netting clustering method is provided. The similarity calculation method has higher differentiation degree and has overcome some shortcomings of previous ones. Second, two hybrid-weighting methods are utilized respectively to calculate expert weights and attribute weights for making the decision-making process more credible and scientific. Finally, the IRIC method is applied to LGDM for dealing with the randomness and uncertainty. In addition, an example is offered to demonstrate the application of the proposed approach. According to a time-consuming test, the proposed method is more suitable to address LGDM in the big data environment.
Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline

In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks.
A federated learning differential privacy algorithm for non-Gaussian heterogeneous data

Multi-center heterogeneous data are a hot topic in federated learning. The data of clients and centers do not follow a normal distribution, posing significant challenges to learning. Based on the assumption that the client data have a multivariate skewed normal distribution, we improve the DP-Fed-mv-PPCA model. We use a Bayesian framework to construct prior distributions of local parameters and use expectation maximization and pseudo-Newton algorithms to obtain robust parameter estimates. Then, the clipping algorithm and differential privacy algorithm are used to solve the problem in which the model parameters do not have a display solution and achieve privacy guarantee. Furthermore, we verified the effectiveness of our model using synthetic and actual data from the Internet of vehicles.
TRD-YOLO: A Real-Time, High-Performance Small Traffic Sign Detection Algorithm

Traffic sign detection is an important part of environment-aware technology and has great potential in the field of intelligent transportation. In recent years, deep learning has been widely used in the field of traffic sign detection, achieving excellent performance. Due to the complex traffic environment, recognizing and detecting traffic signs is still a challenging project. In this paper, a model with global feature extraction capabilities and a multi-branch lightweight detection head is proposed to increase the detection accuracy of small traffic signs. First, a global feature extraction module is proposed to enhance the ability of extracting features and capturing the correlation within the features through self-attention mechanism. Second, a new, lightweight parallel decoupled detection head is proposed to suppress redundant features and separate the output of the regression task from the classification task. Finally, we employ a series of data enhancements to enrich the context of the dataset and improve the robustness of the network. We conducted a large number of experiments to verify the effectiveness of the proposed algorithm. The accuracy of the proposed algorithm is 86.3%, the recall is 82.1%, the mAP@0.5 is 86.5% and the mAP@0.5:0.95 is 65.6% in TT100K dataset, while the number of frames transmitted per second is stable at 73, which meets the requirement of real-time detection.
Population evolution analysis in collective intelligence design ecosystem

The Collective Intelligent Design Ecosystem is a dynamic ecosystem founded on an online design platform that leverages collective intelligence to support the creation of novel products. The system's primary components are its users and designers. Maintaining the system's sustainability requires expanding the scale of the designer and user populations as it evolves to stabilize. However, the unity of ecological interactions between various populations is fragmented in contemporary studies of population-scale evolution, and the parameterization of evolutionary models is illogical. To overcome this gap, this research provides a population evolution model of collective intelligent design incorporating participants' intra- and interspecific ecological connections. The model's validity is verified by the evolutionary simulation of 110 designers and 5990 users of China's largest collective intelligence design platform, the Zhubajie platform, and illuminating conclusions are in turn drawn from this simulation. First, the designer's influence on the user is greater than the user's impact on the designer. Second, keeping current members engaged is more crucial to the system's viability than luring in new ones. Third, fostering collaboration among designers while retaining user competitiveness can promote system growth. Fourth, decreasing the reliance between particular designers and users might hasten the system's evolution.
Blockchain and IoT Based Smart Agriculture and Food Supply Chain System

One of the important areas of study that needs focus on development in all emerging countries is agriculture and its supply chain. Due to the potential health risks, food supply and safety are also attracting global attention due to their significance. Using blockchain and IoT technologies, we have provided a methodology in this study for transforming traditional agriculture into smart farming. All parties participating in the agricultural food supply chain will be given an equal chance under this approach, even if they are not related. To minimize human involvement in data collection, recording, and verification, The smart model incorporates IoT devices. The validation of our unique model is compared to our own technique, which exclusively makes use of blockchain in the monitoring field without the use of IoT devices.
A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System

Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms.
Social engineering with ChatGPT

In this paper we are presenting a possibility of using ChatGPT for preparing environments for executing social engineering based attacks. Very soon after public launching, ChatGPT has shown excellent results in many different topics, answering some general or specific questions, generating code or preparing text templates of even texts on specific topics. Combining those possibilities and good preparedness of the system, it is possible to get everything that is needed for phishing or some other attack only in a few clicks and in a couple of minutes. This paper covers the scenario of creating phishing attack using ChatGPT, with an overview of social engineering attacks and their prevention in general.
An adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion for solving continuous optimization problems

Particle swarm optimization (PSO) is a simple and efficient optimization method that has been used in many fields. However, PSO has some flaws including premature convergence and poor population diversity. To solve these problems of PSO, an adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion (ADPSO) is proposed. The newly proposed ADPSO is based on a dynamic multi-swarm PSO framework that cooperates with stagnation detection mechanism (SDM) and spatial exclusion strategy (SES). Firstly, the whole population is divided into multiple equal sub-swarms, which can be regrouped during evolution. The best particle of each sub-swarm, lbest, is used to evaluate the evolutionary state of each sub-swarm. If the lbest cannot improve its solution continuously without reaching the regroup period, the SDM will be triggered. In this case, a vitality particle is generated to help the stagnant sub-swarm to search for promising areas again. To keep the population diversity, the vitality particle is constructed according to the excellent historical information of all particles in the whole population. Secondly, to enhance the diversity of the population, the SES is proposed to avoid premature adsorption of all sub-swarms. Finally, the effect of ADPSO is evaluated using CEC2013, CEC2017 and four engineering optimization problems. The results shows that the proposed ADPSO is suitable for solving most optimization problems, and ADPSO has better results compared to the state-of-the-art PSO variants (i.e., MSCPSO, HCDMPS) and other popular evolutionary algorithms.