Reviewerly/exHarmony huggingface dataset:
Convex combination multiple populations competitive swarm optimization for moving target search using UAVs

Unmanned aerial vehicles (UAVs) searching for a moving target is a complex search optimization problem that aims to find a path with maximum probability of observing the target. However, as the area where the target may be located increases in size, existing methods tend to cause easy entrapment into local optima, resulting in poor performance. Hence, we propose a Convex Combination Multiple Populations Competitive Swarm Optimization algorithm (CDCSO) in this paper. First, we suggest applying the multiple population strategy to enhance the search ability of the algorithm. The population of interest is divided into two subgroups that each conduct the CSO. Second, we propose a novel convex combination update strategy to coordinate the two subgroups to search jointly for the global optimal solution. At each iteration, this strategy allows the two subgroups to compete and learn from each other, which prevents the population from falling into the local optimum. We then designed new scenarios containing 3 to 6 probability areas to investigate the performance of our proposed algorithm in multiple and complex scenarios. Results: To demonstrate the effectiveness and stability of the proposed algorithm, we compared it with several swarm optimization algorithms from different perspectives. Experimental results showed that the proposed algorithm has superior performance in following and intercepting a high-probability area. To further demonstrate the generality of the convex combination update strategy, we evaluated the strategy in several developed particle swarm optimization algorithms. The results demonstrated that the convex combination update strategy can be integrated into other algorithms to better address the moving target search problem.
Digitizing global supply chains through blockchain

The objective of this paper is to understand the industry perspective towards the benefits of embedding blockchain technology (BCT) in global supply chains (GSC). A qualitative approach with the view of transaction cost economics (TCE) and diffusion of innovation (DOI) theory is applied. Semi-structured interviews with 23 industry experts showcased the benefits of application of BCT into GSC and this yielded interesting themes and sub-themes using the thematic analyses first yielding, the critical issues and challenges in traditional supply chains (TSC) and second, how BCT can contribute to overcome these challenges to support digital GSC; and third, possible challenges adapting BCT. This study advances the supply chain decision making complemented with elements of TCE and DOI theory through the application of BCT. GSC managers can deploy BCT integrated with the Internet of Things to effectively meet worldwide supply chain demands and complex situations.
An interval multi-criteria decision-making model for evaluating blockchain-IoT technology in supply chain networks

Supply Chain (SC) networks benefit from the integration of Blockchain Technology (BT) and the Internet of Things (IoT) in many ways, from data protection and transparency to fraud prevention, traceability, and cost reduction. As the technology is new, there is a lack of knowledge and experience in selecting suitable platforms for SC networks. Choosing the right platform is a difficult and complex task involving multiple and conflicting criteria. This study presents a novel practical approach for evaluating and selecting a BT-IoT platform in SC networks using multi-criteria decision-making. First, an extensive literature review is conducted to determine the most suitable criteria for BT-IoT platform selection. Then, using the interval Weighted Influence Non-linear Gauge System (WINGS) method, the weights of the criteria are calculated, and finally an interval VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique is used to assess the suitability of the platforms. The proposed approach uses the experts' preferences, considers the interdependencies among criteria in the weighting process, and enables implementation in a hierarchical and interconnected network. We present a case study in the food industry to demonstrate the applicability of the proposed model. Additional sensitivity analysis is conducted to exhibit the efficacy of the proposed model in selecting a suitable and robust platform in SC networks.
Path planning in continuous adjacent farmlands and robust path-tracking control of a rice-seeding robot in paddy field

During rice seeding, paddy fields are segmented by embankments to hold water and evenly supply nutrients to the rice. Reasonable path planning from the entry to the exit of continuous adjacent sublands and accurate path tracking in harsh paddy fields are crucial for efficient and high quality work by an autonomous rice-seeding robot. In this study, four entering and exiting modes with continuous adjacent sublands are defined, and the corresponding planning paths for each mode are provided and analyzed. The optimal working path is obtained under the constraints of full coverage without overlap or skip between rows, minimum blank operation areas at the headland and minimum turns while working; The requirements for the width of standardized farmland and the number the farmland is divided into are given. A kinematic model of the rice-seeding robot embodying slip and side slip is built to achieve reliable and accurate path tracking under adverse conditions of paddy fields. The equivalent disturbances caused by slip and side slip are estimated using a nonlinear observer. Combined with a nonlinear observer, a fast terminal sliding-mode controller is designed for the path tracking of the rice-seeding robot in paddy fields. The control algorithm is validated through simulations and field tests.
BSAF: A blockchain-based secure access framework with privacy protection for cloud-device service collaborations

In an open Internet-of-Things (IoT) environment, cloud-device collaborative development brings more functional services to users. However, privacy disclosure risks concerning service provision and users' accessing behavior also increase. Traditional privacy protection approaches involve a centralized system with a third-party administrative server, which has the risk of single-point failures and overlooks the issue of privacy information leakage concerning users' behaviors. Blockchain is a decentralized and traceable technology that provides a reliable solution for secure access by users. This paper proposes a novel blockchain-based secure access framework (BSAF) for cloud-device service collaborations with privacy protection. Specifically, a key matrix encryption mechanism is used to protect the privacy of users' behaviors, and a fully homomorphic encryption mechanism is designed to protect the privacy of service content. Two smart contracts are designed: a request verification smart contract to verify the access rights of users, and a behavior punishment smart contract to audit users' access behaviors. Comprehensive experiments on the Ethereum blockchain network show that the proposed BSAF framework outperforms existing schemes in latency reduction and cost saving, and is more suitable for low-profile IoT devices.
A hybrid indicator for realistic blurred image quality assessment

Blurriness is annoying yet common in digital images. Many sharpness assessment indicators using handcrafted features achieve impressive results on synthesized blurring images, while room exists for improvement on realistic datasets. This study presents a hybrid indicator in which no-reference indicators perform as mid-level feature extractors and their outputs are selected using a consensus-based method for discriminative ones. On realistic image datasets, 15 off-the-shelf indicators are explored, and experimental results reveal that the hybrid indicator obtains considerable improvement (≥ 21.5%, BID2011; ≥ 11.6%, CID2013; ≥ 7.1%, LIVE Challenge; and ≥ 11.6%, KonIQ-10k) compared to the baseline indicator. Meanwhile, the indicator requires more features for representation of diverse distortions (CID2013, LIVE Challenge and KonIQ-10k) than different blurriness (BID2011). Four regression models are investigated, and fitting neural network leads to overall better results. Realistic image quality assessment is challenging, fusion of existing indicators improves the performance, while to develop advanced indicators remains desirable.
A traceable and revocable decentralized multi-authority privacy protection scheme for social metaverse

Intelligent identification of close friends is the mainstream approach to protect users' multi-dimensional data in the social metaverse. It provides a fine-grained matching service for users. However, users enjoy these benefits at the cost of leaking personal multi-dimensional data. The existing social metaverse-based intelligent identification of close friends privacy protection models typically use centralized mechanisms. This will limit users' control over their data and make them vulnerable to privacy leakage and data misuse. To address these problems, this paper proposes a traceable and revocable decentralized multi-authority privacy protection scheme (TRDM) for social metaverse. The scheme establishes multiple authority centers based on users' multi-dimensional data, overcoming single point of failure and performance bottlenecks. In addition, TRDM partially hides access policies and supports multi-dimensional attribute access control in social metaverse environment. By introducing leaf node values of binary trees, TRDM achieves the traceability and completes the revocation of malicious users by updating part of the ciphertext. Security analysis and experiments show that TRDM satisfies the requirement of static security under the random prediction model and has better performance in the revocation of malicious users.
English–Assamese neural machine translation using prior alignment and pre-trained language model

In a multilingual country like India, automatic natural language translation plays a key role in building a community with different linguistic people. Many researchers have explored and improved the translation process for high-resource languages such as English, German, etc., and achieved state-of-the-art results. However, the unavailability of adequate data is the prime obstacle to automatic natural language translation of low-resource north-eastern Indian languages such as Mizo, Khasi, and Assamese. Though the recent past has witnessed a deluge in several automatic natural language translation systems for low-resource languages, the low values of their evaluation measures indicate the scope for improvement. In the recent past, the neural machine translation approach has significantly improved translation quality, and the credit goes to the availability of a huge amount of data. Subsequently, the neural machine translation approach for low-resource language is underrepresented due to the unavailability of adequate data. In this work, we have considered a low-resource English–Assamese pair using the transformer-based neural machine translation, which leverages the use of prior alignment and a pre-trained language model. To extract alignment information from the source–target sentences, we have used the pre-trained multilingual contextual embeddings-based alignment technique. Also, the transformer-based language model is built using monolingual target sentences. With the use of both prior alignment and a pre-trained language model, the transformer-based neural machine translation model shows improvement, and we have achieved state-of-the-art results for the English-to-Assamese and Assamese-to-English translation, respectively.
Hospital-wide natural language processing summarising the health data of 1 million patients

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task.
Survey of Distributed and Decentralized IoT Securities: Approaches Using Deep Learning and Blockchain Technology

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.
Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks

Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis of breast cancer (BC) frequently makes the use of several imaging techniques. On the other hand, incorrect identification might occasionally result in unnecessary therapy and diagnosis. Therefore, the accurate identification of breast cancer can save a significant number of patients from undergoing unnecessary surgery and biopsy procedures. As a result of recent developments in the field, the performance of deep learning systems used for medical image processing has showed significant benefits. Deep learning (DL) models have found widespread use for the aim of extracting important features from histopathologic BC images. This has helped to improve the classification performance and has assisted in the automation of the process. In recent times, both convolutional neural networks (CNNs) and hybrid models of deep learning-based approaches have demonstrated impressive performance. In this research, three different types of CNN models are proposed: a straightforward CNN model (1-CNN), a fusion CNN model (2-CNN), and a three CNN model (3-CNN). The findings of the experiment demonstrate that the techniques based on the 3-CNN algorithm performed the best in terms of accuracy (90.10%), recall (89.90%), precision (89.80%), and f1-Score (89.90%). In conclusion, the CNN-based approaches that have been developed are contrasted with more modern machine learning and deep learning models. The application of CNN-based methods has resulted in a significant increase in the accuracy of the BC classification.
Exploring the Impact of AI-Based Cyber Security Financial Sector Management

Cyber threats are attempts to secure unauthorized access to, change, or delete private information, to demand money from victims, or to disrupt business. Cybercrime includes everything from identity theft, malware threats, email and online fraud, to bank fraud. Businesses and individuals use this method to guard their data centers and other digital systems. The lack of scalability, sluggish response times, and inability to spot advanced and insider threats are among some of the problems with conventional approaches to network security. These flaws highlight the need for research to build more efficient and all-encompassing security methods to guard against the expanding variety of network attacks. Cybercriminals use AI and data poisoning, as well as model theft strategies to automate their attacks. A cyber security technique based on artificial intelligence is presented in this study for financial sector management (CS-FSM). In order to map and prevent unexpected risks from devouring a business, artificial intelligence is one of the best technologies. Using the proposed technique, cyberattack problems can be classified and solved. To ensure the security of financial sector information, algorithms such as the Enhanced Encryption Standard (EES) encrypt and decrypt data. By learning from the training data, the K-Nearest Neighbor (KNN) algorithm produces predictions. In the financial sector, it is used to detect and stop malware attacks. The proposed method increases cyber security systems’ performance by increasing their defense against cyberattacks. CS-FSM enhances data privacy (18.3%), scalability (17.2%), risk reduction (13.2%), data protection (16.2%), and attack avoidance (11.2%) ratios.
Color image encryption base on a 2D hyperchaotic enhanced Henon map and cross diffusion

The parameter range of traditional 2D chaotic maps is small, limiting the sequence generation performance. In order to improve security and efficiency, it is necessary to develop a new 2D chaotic map for image encryption. This paper proposes an enhanced version of the Henon Map, called 2D-HHMSC, which has better sequence generation performance than the Henon Map under the same parameters. In addition, 2D-HHMSC has ample parameter space, enhancing the generated sequences' randomness. The proposed method is a new color image encryption algorithm. To prevent the algorithm from being subjected to brute force attacks, two hash functions are used to generate the parameters and initial values of the Arnold map and 2D-HHMSC. The key space of the algorithm can reach 2̂1024. In the scrambling phase, the three channels of the color image are scrambled using Arnold maps with different parameters and then combined into a grayscale image. In order to fully diffuse the scrambled image, use the key stream generated by 2D-HHMSC to diffuse the grayscale image, starting from the center position of the grayscale image The correlation coefficients of the proposed encryption algorithm are 0.002, 0.0012, and −0.0006, and the information entropy is 7.9993. Experimental results show that the algorithm can resist common attacks, proving its robustness and reliability.
Sparse spatial transformers for few-shot learning

Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose useful local information. Many few-shot learning methods have recently addressed this challenge using deep descriptors and learning a pixel-level metric. However, using deep descriptors as feature representations may lose image contextual information. Moreover, most of these methods independently address each class in the support set, which cannot sufficiently use discriminative information and task-specific embeddings. In this paper, we propose a novel transformer-based neural network architecture called sparse spatial transformers (SSFormers), which finds task-relevant features and suppresses task-irrelevant features. Particularly, we first divide each input image into several image patches of different sizes to obtain dense local features. These features retain contextual information while expressing local information. Then, a sparse spatial transformer layer is proposed to find spatial correspondence between the query image and the full support set to select task-relevant image patches and suppress task-irrelevant image patches. Finally, we propose using an image patch-matching module to calculate the distance between dense local representations, thus determining which category the query image belongs to in the support set. Extensive experiments on popular few-shot learning benchmarks demonstrate the superiority of our method over state-of-the-art methods.
Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.