Reviewerly/exHarmony huggingface dataset:
A Patient-Centric Healthcare Framework Reference Architecture for Better Semantic Interoperability Based on Blockchain, Cloud, and IoT

Due to the distributed and non-integrated nature of the healthcare systems which results from the application-centric view it leads to a challenging task to manage healthcare data ex change (heterogeneity problem).On the other hand, Blockchain technologies are emerging as promising and cost -effective means to meet some of these requirements due to their inherent design properties, such as secure cryptography and a resilient peer-to-peer network.Likewise, Blockchain-based applications can benefit the healthcare domain via their properties of asset sharing, and audit trails of data access.Existing work mainly pays attention to centralized and blockchain-based mechanisms.But it doesn't realize the increase need for better data interoperability amount multiple healthcare systems and services.This requires shifting from the applicationcentric solutions toward the patient-centric solutions.This paper presents A secure and efficient fram ework based on Blockchain, Cloud, and IoT named Patient -Centric Healthcare Framework (PCH) for better healthcare systems interoperability.A tiered-based architecture (5 tiers) with collaboration is designed for the feasible realization of PCH.Also, the design and implementation aspects start from the layering diagram, system context, and detailed reference architecture that emphasizes the detailed component topology and interactions within the framework.An electronic medical record is used to show how he althcare data is processed with the required security considerations.Then, an evaluation of PCH against the existing Blockchain-based healthcare frameworks is conducted.The results analysis demonstrates that PCH offers practical solutions to protect healthcare data and support efficient data sharing with better interoperability
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community's interest in this emerging field.
Sentiment Analysis of Consumer Reviews Using Deep Learning

Internet and social media platforms such as Twitter, Facebook, and several blogs provide various types of helpful information worldwide. The increased usage of social media and e-commerce websites is constantly generating a massive volume of data about image/video, sound, text, etc. The text among these is the most significant type of unstructured data, requiring special attention from researchers to acquire meaningful information. Recently, many techniques have been proposed to obtain insights from these data. However, there are still challenges in dealing with the text of enormous size; therefore, accurate polarity detection of consumer reviews is an ongoing and exciting problem. Due to this, it is challenging to derive exact meanings from the textual data from consumer reviews, comments, tweets, posts, etc. Previously, a reasonable amount of work has been conducted to simplify the extraction of exact meanings from these data. A unique technique that includes data gathering, preprocessing, feature encoding, and classification utilizing three long short-term memory variations is presented to address sentiment analysis problems. Analysing appropriate data collection, preprocessing, and classification is crucial when interpreting such data. Different textual datasets were used in the studies to gauge the importance of the suggested models. The proposed technique of predicting sentiments shows better, or at least comparable, results with less computational complexity. The outcome of this work shows the significant importance of sentiment analysis of consumer reviews and social media content to obtain meaningful insights.
A new movement strategy of grey wolf optimizer for optimization problems and structural damage identification

In this paper, an improved Grey Wolf Optimizer (GWO) algorithm, termed LGWO, is introduced. The enhanced version is interesting and complementary in terms of the direction of movement of the leader wolf, and a special parameter that allows the faster wolves to prey position. The Lévy flight is employed as a special navigation solution for alpha, beta, and delta wolf. In this way, the leader wolf equips a powerful tool to deal with the local search problem. A new principle illustrates the behaviour of omega wolf in hunting is also added to enhance the convergence speed of this algorithm. To investigate the performance of LGWO, a series of problems, namely 23 classical benchmarks, a set of CEC 2019 functions, and three engineering problems, is investigated. Furthermore, LGWO is employed to study structural damage identification in high-dimensional problems. The research appears to show that the performance of LGWO is substantially increased.
Wavelet-based neural network with genetic algorithm optimization for generation prediction of PV plants

In order to solve the problem of uncertainty of photovoltaic power generation forecast, the prediction accuracy of photovoltaic power station power generation is further improved. In this paper, a wavelet neural network (WNN) based genetic algorithm (GA) is proposed to optimize the output forecasting method of photovoltaic power plants. It adopts the wavelet basis function as the transfer function of the network, and regards the network connection weight, the scaling factor and the translation factor of the wavelet function as a genetic individual. Then, it obtains the optimal initial parameters of the network through individual optimization based on genetic algorithm, and then imports it into the network. The results show that: under four typical weather types, the experimental data measured by photovoltaic power plants every half an hour are imported into four types of back-propagation artificial neural network (BP-ANN), WNN, GA-BP and GA-WNN network for simulation and prediction. The GA-WNN network prediction model outperforms the other three prediction methods in considering prediction error, relative error, other error evaluation methods and prediction reliability. The GA-WNN network is more suitable for the power generation forecast of photovoltaic power plants.
A Survey of Trajectory Planning Techniques for Autonomous Systems

This work offers an overview of the effective communication techniques for space exploration of ground, aerial, and underwater vehicles. We not only comprehensively summarize the trajectory planning, space exploration, optimization, and other challenges encountered but also present the possible directions for future work. Because a detailed study like this is uncommon in the literature, an attempt has been made to fill the gap for readers interested in path planning. This paper also includes optimization strategies that can be used to implement terrestrial, underwater, and airborne applications. This study addresses numerical, bio-inspired, and hybrid methodologies for each dimension described. Throughout this study, we endeavored to establish a centralized platform in which a wealth of research on autonomous vehicles (on the land and their trajectory optimizations), airborne vehicles, and underwater vehicles, is published.
Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm

Aiming at the problems of low global path quality and poor dynamic obstacle avoidance performance in underwater three-dimensional AUV autonomous path planning, an AUV global path planning method based on an adaptive genetic algorithm (AGA) is proposed in this paper. First, the traditional genetic algorithm are optimized to reduce the time of global path generation and improve the quality of global path generation. The path optimization strategy based on the collision detection mechanism is used to optimize the global path. Then, a local obstacle avoidance strategy based on the vector artificial potential field method (VAPF) is proposed. The space vector method is used to improve the calculation method of the resultant force direction to improve the calculation efficiency of the algorithm. Finally, the key path point is used as the local target point, and local obstacle avoidance is carried out under the guidance of the vector artificial potential field method. The simulation results show that the adaptive genetic algorithm can generate effective and high-quality global paths in a three-dimensional underwater environment. The new obstacle avoidance strategy can make AUVs effectively avoid all kinds of obstacles and reduce the obstacle avoidance cost of AUVs.
Uncanny Valley Effects on Chatbot Trust, Purchase Intention, and Adoption Intention in the Context of E-Commerce: The Moderating Role of Avatar Familiarity

This study investigates the effect of chatbot humanization on the perception of eeriness, trust, and users’ behavioral intention. Specifically, this study employed a 2 (humanization of chatbot agent avatar: hyperrealistic-animated vs. cartoonish-still) × 2 (avatar familiarity: celebrity avatar vs. non-celebrity avatar) between-subjects experiment (N = 185), in which participants were asked to purchase a laptop from an e-commerce vendor by interacting with a chatbot agent. Based on predictions from the uncanny valley effect hypothesis (UVE), enhancing the human likeness of a chatbot agent through visual realism and animacy was predicted to negatively influence users’ trust in the chatbot agent and behavioral intention as a consequence of the activation of a negative affective state (i.e., a feeling of eeriness). Consistent with our predictions, the results from PLS-SEM showed that (a) enhancing the human likeness of a chatbot agent significantly increased users’ feeling of eeriness, (b) the feeling of eeriness negatively influenced users’ trust in the chatbot agent, c) trust, determined by the feeling of eeriness, significantly affected users’ purchase intention and willingness to reuse the chatbot, and d) the relationship between humanization and eeriness significantly moderated by the familiarity of the chatbot avatar. We discuss the theoretical implications of the current study on UVE as well as its practical implications for the implementation of anthropomorphized chatbot agents in the e-commerce context.
A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber-physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information.
Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Indian history is derived from ancient writings on the inscriptions, palm leaves, copper plates, coins, and many more mediums. Epigraphers read these inscriptions and produce meaningful interpretations. Automating the process of reading is the interest of our study, and in this paper, segmentation to detect text on digitized inscriptional images is dealt in detail. Character segmentation from epigraphical images helps in optical character recognizer in training and recognition of old regional scripts. Epigraphical images are drawn from estampages containing scripts from various periods starting from Brahmi in the 3rd century BC to the medieval period of the 15th century AD. The scripts or characters present in digitized epigraphical images are illegible and have complex noisy background textures. To achieve script/text segmentation, region-based convolutional neural network (CNN) is employed to detect characters in the images. Proposed method uses selective search to identify text regions and forwards them to trained CNN models for drawing feature vectors. These feature vectors are fed to support vector machine classifiers for classification and recognize text by drawing a bounding box based on confidence score. Alexnet, VGG16, Resnet50, and InceptionV3 are used as CNN models for experimentation, and InceptionV3 performed well with good results. A total of 197 images are used for experimentation, out of which 70 samples are of printed denoised epigraphical images, 40 denoised estampage images, and 87 noisy estampage images. The segmentation result of 74.79% for printed denoised epigraphical images, 71.53 % for denoised estampage epigraphical images, and 18.11% for noisy estampage images are recorded by InceptionV3. The segmented characters are used for epigraphical applications like period/era prediction and recognition of characters. FAST and FASTER region-based design approach was also tested and illustrated in this paper.
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models

State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training. This approach has gained popularity in recent years, and researchers have demonstrated prompts that achieve strong accuracy on specific NLP tasks. However, finding a prompt for new tasks requires experimentation. Different prompt templates with different wording choices lead to significant accuracy differences. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts. We developed a workflow that allows users to first focus on model feedback using small data before moving on to a large data regime that allows empirical grounding of promising prompts using quantitative measures of the task. The tool then allows easy deployment of the newly created ad-hoc models. We demonstrate the utility of PromptIDE (demo: http://prompt.vizhub.ai) and our workflow using several real-world use cases.
Deep multiagent reinforcement learning: challenges and directions

Abstract This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more complex as (a) the future rewards depend on multiple players’ joint actions and (b) the computational complexity increases. We present the most common multiagent problem representations and their main challenges, and identify five research areas that address one or more of these challenges: centralised training and decentralised execution, opponent modelling, communication, efficient coordination, and reward shaping. We find that many computational studies rely on unrealistic assumptions or are not generalisable to other settings; they struggle to overcome the curse of dimensionality or nonstationarity. Approaches from psychology and sociology capture promising relevant behaviours, such as communication and coordination, to help agents achieve better performance in multiagent settings. We suggest that, for multiagent RL to be successful, future research should address these challenges with an interdisciplinary approach to open up new possibilities in multiagent RL.
Study on the Complex Dynamical Behavior of the Fractional-Order Hopfield Neural Network System and Its Implementation

The complex dynamics analysis of fractional-order neural networks is a cutting-edge topic in the field of neural network research. In this paper, a fractional-order Hopfield neural network (FOHNN) system is proposed, which contains four neurons. Using the Adomian decomposition method, the FOHNN system is solved. The dissipative characteristics of the system are discussed, as well as the equilibrium point is resolved. The characteristics of the dynamics through the phase diagram, the bifurcation diagram, the Lyapunov exponential spectrum, and the Lyapunov dimension of the system are investigated. The circuit of the system was also designed, based on the Multisim simulation platform, and the simulation of the circuit was realized. The simulation results show that the proposed FOHNN system exhibits many interesting phenomena, which provides more basis for the study of complex brain working patterns, and more references for the design, as well as the hardware implementation of the realized fractional-order neural network circuit.
Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement

Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an ill-posed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the features from wavelet domain. Furthermore, our HWMNet has competitive performance results on two image enhancement datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/FanChiMao/HWMNet.
A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Fifth generation (5G) network and beyond envision massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with the spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL)-based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect – background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues – necessary to elucidate this topic to the reader in an encyclopedic manner.
Green Blockchain based IoT for secured supply chain of hazardous materials

There is a wide recognition that Blockchain technology will play a central role in many sectors. In fact, this technology is a promising opportunity to ensure security, traceability, privacy and transparency in most digitized human activities. The supply chain of hazardous materials is a typical sensitive industrial activity that requires a high level of vigilance. The various stakeholders of such supply chain need to share crucial data using high secure channels while ensuring traceability. However, Blockchain is currently an energy-intensive technology. In this context, the challenge is to minimize the risk related to the hazardous materials supply chain using an eco-friendly and cost effective Blockchain. In this paper, we propose a green-Blockchain based framework for the hazardous materials supply chain. Two mathematical models are proposed for the energy management consumed by Blockchain transactions. The first one minimizes the total consumed energy by a set of servers by providing the optimal assignment of computing tasks over the time periods, while the second one minimizes both the CO2 emissions and the cost of a grid energy mix. To prove the applicability of the proposed framework, a Blockchain prototype is implemented to simulate an IoT-based supply chain of hazardous materials. The results show that the proposed system may help to ensure the security of dangerous goods transportation. Furthermore, the energy management using optimization modeling would provide CO2 emissions reduction and thus ensure sustainability of Blockchain transactions.