Reviewerly/exHarmony huggingface dataset:
A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes a novel parameter estimation method to address this problem. Firstly, the properties of the piecewise nonlinear amplitude transform (PNAT) are derived. This manuscript verifies that the PNAT can retain phase information of the LFM signal while suppressing the impulsive noise. Subsequently, a new concept known as piecewise nonlinear amplitude transform parametric symmetric instantaneous autocorrelation function (PNAT-PSIAF) is proposed. Based on this concept, a novel method called piecewise nonlinear amplitude transform Lv’s distribution (PNAT-LVD) is proposed to estimate the centroid frequency and chirp rate of the LFM signal. The simulations show that the proposed algorithm can effectively suppress the impulsive noise without prior knowledge of the noise for both the single-component and double-component LFM signal. In addition, two parameters of the LFM signal can be precisely estimated by the proposed method under low generalized signal-to-noise ratios (GSNR). The stronger the impulsive characteristics of the noise, the better the performance of the algorithm.
Enhancing federated learning robustness in adversarial environment through clustering Non-IID features

Federated Learning (FL) enables many clients to train a joint model without sharing the raw data. While many byzantine-robust FL methods have been proposed, FL remains vulnerable to security attacks such as poisoning attacks and evasion attacks due to its distributed adversarial environment. Additionally, real-world training data used in FL are usually Non-Independent and Identically Distributed (Non-IID), which further weakens the robustness of the existing FL methods (such as Krum, Median, Trimmed-Mean, etc.), thereby making it possible for a global model in FL to be broken in extreme Non-IID scenarios. In this work, we mitigate the aforementioned weaknesses of existing FL methods in Non-IID and adversarial scenarios by proposing a new FL framework called Mini-Federated Learning (Mini-FL). Mini-FL follows the general FL approach but considers the Non-IID sources of FL and aggregates the gradients by groups. Specifically, Mini-FL first performs unsupervised learning for the gradients received to define the grouping policy. Then, the server divides the gradients received into different groups according to the grouping policy defined and performs byzantine-robust aggregation. Finally, the server calculates the weighted mean of gradients from each group to update the global model. Owning the strong generality, Mini-FL can utilize the most existing byzantine-robust method. We demonstrate that Mini-FL effectively enhances FL robustness and achieves greater global accuracy than existing FL methods when against security attacks and in Non-IID settings.
Unconventional Integrated Photonic Accelerators for High-Throughput Convolutional Neural Networks

We provide an overview of the rapidly evolving landscape of integrated photonic neuromorphic architectures, specifically targeting the implementation of convolutional neural networks. The exploding research momentum stems from the well-known advantages of photonic circuits compared to digital electronics, and at the same time, it is driven by the massive need for cognitive image/video processing. In this context, we provide a detailed literature review on photonic cores operating as convolutional neural networks, covering either the functionality of a conventional neural network or its spiking counterpart. Moreover, we propose 2 alternative photonic approaches that refrain from simply transferring neural network concepts directly into the optical domain; instead, they focus on fusing photonic, digital electronic, and event-based bioinspired processing to optimally exploit the virtues of each scheme. These approaches can offer beyond state-of-the-art performance while relying on realistic, scalable technology. The first approach is based on a photonic integrated platform and a bioinspired spectrum-slicing technique. The photonic chip allows feature extraction through optical filtering with low power consumption and an equivalent computational efficiency of 72 femtojoules per multiply-and-accumulate operation for 5-bit precision. When combined with typical digital neural networks, an almost 5-fold reduction in the number of parameters was achieved with a minor loss of accuracy compared to established convolutional neural networks. The second approach follows a bioisomorphic route in which miniaturized spiking laser neurons and unsupervised bioinspired training are unified in a deep architecture, revealing a noise-resilient and power-efficient proposition.
Royalty-Friendly Digital Asset Exchanges on Blockchains

The distribution of royalties associated with the exchange of digital assets, especially Non-Fungible Tokens (NFTs), is now more than ever a strong point of contention.Between conceptual disagreements and technical limitations, actors have implemented a variety of solutions tailored to their needs.In the process, creators and buyers have lost the possibility of transparent, trusted, and interoperable exchanges of said assets, often having to compromise to connect with the rest of the community.This study deals with the automatic distribution of royalty payments.We first investigate the current day limitations, and formally state their underlying requirements, before advancing a royalty-friendly NFT marketplaceagnostic trading framework.The advanced solution, referred to as the RM-TLSC -Royalty Management Token-Level Smart Contract, establishes synergies between the token and Smart Contract paradigms, thus ensuring royalties are managed throughout the life cycle of the asset.A comprehensive, open-source software implementation is provided for the Ethereum blockchain, while the generality of the approach is cross-checked by an open-source proof of concept for the Tezos blockchain.The effectiveness of the results is illustrated through a case-study related to ISO 21000-23 Smart Contracts for Media standard.
Disjunctive Threshold Networks for Tabular Data Classification

While neural networks have been achieving increasingly significant excitement in solving classification tasks such as natural language processing, their lack of interpretability becomes a great challenge for neural networks to be deployed in certain high-stakes human-centered applications. To address this issue, we propose a new approach for generating interpretable predictions by inferring a simple three-layer neural network with threshold activations, so that it can benefit from effective neural network training algorithms and at the same time, produce human-understandable explanations for the results. In particular, the hidden layer neurons in the proposed model are trained with floating point weights and binary output activations. The output neuron is also trainable as a threshold logic function that implements a disjunctive operation, forming the logical-OR of the first-level threshold logic functions. This neural network can be trained using state-of-the-art training methods to achieve high prediction accuracy. An important feature of the proposed architecture is that only a simple greedy algorithm is required to provide an explanation with the prediction that is human-understandable. In comparison with other explainable decision models, our proposed approach achieves more accurate predictions on a broad set of tabular data classification datasets.
Deep Reinforcement Learning for the Visual Servoing Control of UAVs with FOV Constraint

Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera.
Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.
Face morphing attack detection based on high-frequency features and progressive enhancement learning

Face morphing attacks have become increasingly complex, and existing methods exhibit certain limitations in capturing fine-grained texture and detail changes. To overcome these limitation, in this study, a detection method based on high-frequency features and progressive enhancement learning was proposed. Specifically, in this method, first, high-frequency information are extracted from the three color channels of the image to accurately capture the details and texture changes. Next, a progressive enhancement learning framework was designed to fuse high-frequency information with RGB information. This framework includes self-enhancement and interactive-enhancement modules that progressively enhance features to capture subtle morphing traces. Experiments conducted on the standard database and compared with nine classical technologies revealed that the proposed approach achieved excellent performance.
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Cryptocurrency Price Prediction Model using Deep Learning

Cryptocurrencies have gained immense popularity in recent years as an emerging asset class, and their prices are known to be highly volatile. Predicting cryptocurrency prices is a difficult task due to their complex nature and the absence of a central authority. In this paper, our proposal is to employ Long Short-Term Memory (LSTM) networks, a type of deep learning technique to forecast the prices of cryptocurrencies. We use historical price data and technical indicators as inputs to the LSTM model, which learns the underlying patterns and trends in the data. To improve the accuracy of the predictions, we also incorporate a Change Point Detection (CPD) technique using the Pruned Exact Linear Time (PELT) algorithm. This method allows us to detect significant changes in cryptocurrency prices and adjust the LSTM model accordingly, leading to better predictions. We evaluate our approach predominantly on Bitcoin cryptocurrency, but the model can be implemented on other cryptocurrencies provided there are valid historical price data. Our experimental results show that our proposed model outperforms the baseline LSTM algorithm, achieving higher accuracy and better performance in terms of Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Our research findings suggest that combining deep learning techniques such as LSTM with change point detection techniques such as PELT can improve cryptocurrency price prediction accuracy and have practical implications for investors, traders, and financial analysts.
A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid

With the advancement of smart devices, the operation and communication of smart grids have become increasingly efficient. Many smart devices such as smart meters, smart transformers, and smart grid controllers are already widely used in smart grids. Thus, a series of complex architectures and a series of communication modes have been formed. However, these smart devices will be exposed to various cyber attacks such as distributed denial of service (DDoS) attack and replay attack. This is because they are open and dynamic. Therefore, there are serious security problems in the complex architectures and the communication modes. In this paper, we propose a multi-domain authentication mechanism based on blockchain cooperation to maintain the security of smart devices. In this mechanism, we propose a series of methods and algorithms, which include initialization method based on blockchain cooperative authentication, dynamic change method of intelligent devices and information, cross-domain authentication algorithm, and cross-domain key cooperative algorithm. To demonstrate the security and effectiveness of our proposed mechanism, we analysed its security and conducted a series of simulation experiments. The analysis and simulation experiments show that our proposed approach is secure and effective.
Weapon Detection in Surveillance Videos Using YOLOV8 and PELSF-DCNN

Weapon detection (WD) provides early detection of potentially violent situations. Despite deep learning (DL) algorithms and sophisticated closed-circuit television (CCTVs), detecting weapons is still a difficult task. So, this paper proposes a WD model using PELSF-DCNN. Initially, the input video is converted into frames and pre-processed. The objects in the pre-processed frames are detected using the YOLOv8. In meantime, motion estimation is done using the DS algorithm in the pre-processed images to cover all the information. Then, the detected weapons undergo a sliding window process by considering the motion estimated frames. The silhouette score is calculated for detected humans and other objects. Now, the features are extracted and the important features are selected using the CSBO algorithm. The selected features and the output of YOLOv8 are given to the PELSF-DCNN classifier. Finally, the confidence score is calculated for the frame to define the number of weapons. In an experimental evaluation, the proposed method is found to be more efficient than the existing methods.
Conversational AI Chatbot for HealthCare

Health is a state of total physical, mental, and social wellbeing. chatbots have been applied to this industry frequently and in a variety ofways in the past, there is still room for more inventive uses. Healthcareconversational AI use cases are flexible and may be tailored to the industry. Patients might use them to gain additional knowledge about their disease, the therapies that are available, or even their insurance coverage. Because research has shown that healthcare chatbots can improve patient satisfaction and significantly reduce wait times, many healthcare organisations are considering incorporating them into their operations. Chatbots for healthcare can be used for a number of purposes, such as monitoring, anonymity, personalization, in-person involvement, and more. In this case study, the user's input on the patient's symptoms will be used to determine the patient's likely ailment type. According on the type of sickness, precautions will be suggested, and the patient will be sent to a doctor who specialises in that field. A sequential model was utilised to extract the text's symptoms, and the KNN method was then applied to predict the patient's ailment type. 1
Orientation Control Design of a Telepresence Robot: An Experimental Verification in Healthcare System

Automation in the modern world has become a necessity for humans. Intelligent mobile robots have become necessary to perform various complex tasks in healthcare and industry environments. Mobile robots have gained attention during the pandemic; human–robot interaction has become vibrant. However, there are many challenges in obtaining human–robot interactions regarding maneuverability, controllability, stability, drive layout and autonomy. In this paper, we proposed a stability and control design for a telepresence robot called auto-MERLIN. The proposed design simulated and experimentally verified self-localization and maneuverability in a hazardous environment. A model from Rieckert and Schunck was initially considered to design the control system parameters. The system identification approach was then used to derive the mathematical relationship between the manipulated variable of robot orientation control. The theoretical model of the robot mechanics and associated control were developed. A design model was successfully implemented, analyzed mathematically, used to build the hardware and tested experimentally. Each level takes on excellent tasks for the development of auto-MERLIN. A higher level always uses the services of lower levels to carry out its functions. The proposed approach is comparatively simple, less expensive and easily deployable compared to previous methods. The experimental results showed that the robot is functionally complete in all aspects. A test drive was performed over a given path to evaluate the hardware, and the results were presented. Simulation and experimental results showed that the target path is maintained quite well.
Strategies for Dealing With Software Product Management Challenges

Software product management, which involves multiple processes and responsibilities and links to many activities within the organisation, increases the success rate of IT projects. However, the adoption of product management activities and the implementation of the software product manager role itself varies between companies. This research explores the topic of software product managers’ strategies for dealing with challenges in their work. The aim of the study is to identify and evaluate solutions to the main problems that affect the software product management process and to provide guidelines for dealing with them. For this purpose, a focus group method was adopted and 47 software product managers participated in 15 focus groups. This was followed by a survey to assess the comprehensibility and effectiveness of the solutions identified. The paper proposes a list of 39 solutions to the 5 most common problems in the work of product managers. Furthermore, based on the identified solutions and the results of discussions with experienced professionals during the focus groups, guidelines for these problems were also developed. These guidelines can be used by product managers as well as other roles working in the product development team to create their own strategies for overcoming software product management challenges and improving software engineering practices. Finally, the solutions and guidelines presented, when combined with a list of problems identified in previous research, will form a Software Product Management Guide– a framework currently under construction, as part of wider research, for product development teams to improve software engineering practices.
Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques

The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications.