Reviewerly/exHarmony huggingface dataset:
Federated optimization via knowledge codistillation

Federated learning is an attractive distributed learning paradigm, which allows resource-constrained edge computing devices to cooperatively train machine learning models, while keeping data locally. However, the non-IID data distribution across devices is one of the main challenges that affect the performance of federated optimization algorithms. Inspired by knowledge distillation, this paper proposes a federated optimization algorithm, Federated Codistillation (FedCodl), in which a distillation term is added to the local objective function, so that local models can be trained on outputs of the global model. We further extend FedCodl to Federated Two-way Codistillation (Fed2Codl) to personalize local models for each device, meanwhile the global model is retained and iteratively updated in parallel. Then, we theoretically provide convergence guarantees for our approaches when learning strongly convex and smooth models. Finally, extensive experiments on federated benchmark datasets demonstrate that our approaches can achieve superior performance under the challenge of multiple non-IID data distribution settings on traditional and deep learning architectures, in terms of classification accuracy and communication efficiency. The biggest accuracy improvement against the standard Federated Averaging (FedAvg) framework is up to 22.26% over all communication rounds. Our results show the potential of the proposed approaches for further applications in federated learning.
Visual Attention and Cognitive Archaeology: An Eye-Tracking Study of Palaeolithic Stone Tools

The study of lithic technology can provide information on human cultural evolution. This article aims to analyse visual behaviour associated with the exploration of ancient stone artefacts and how this relates to perceptual mechanisms in humans. In Experiment 1, we used eye tracking to record patterns of eye fixations while participants viewed images of stone tools, including examples of worked pebbles and handaxes. The results showed that the focus of gaze was directed more towards the upper regions of worked pebbles and on the basal areas for handaxes. Knapped surfaces also attracted more fixation than natural cortex for both tool types. Fixation distribution was different to that predicted by models that calculate visual salience. Experiment 2 was an online study using a mouse-click attention tracking technique and included images of unworked pebbles and 'mixed' images combining the handaxe's outline with the pebble's unworked texture. The pattern of clicks corresponded to that revealed using eye tracking and there were differences between tools and other images. Overall, the findings suggest that visual exploration is directed towards functional aspects of tools. Studies of visual attention and exploration can supply useful information to inform understanding of human cognitive evolution and tool use.
Optimal Deep Neural Network-Based Model for Answering Visual Medical Question

Over the last few years, the amount of available information has increased exponentially in all professional fields, including the medical field. Modern-day patients have access to a wealth of medical information, whether it be from brochures, newspapers, television campaigns, or internet documents. To facilitate and accelerate the search for medical information, more precise systems have been implemented, such as visual question-and-answer systems. A visual question-and-answer system is designed to provide direct and precise answers to questions asked in natural language. In this context, we propose an optimal deep neural network model based on an adaptive optimization algorithm, which takes medical images and natural language questions as input, then provides precise answers as output. Our model starts by classifying medical questions following an embedding phase. We then use a deep learning model for visual and textual feature extraction and emergence. In this paper, we aim to maximize the accuracy rate and minimize the number of epochs in order to accelerate the process. This is a multi-objective optimization problem. The selection of deep learning model parameters, such as epoch number and batch size, is an essential step in improving the model, thus, we use an adaptive genetic algorithm to determine the optimal deep learning parameters. Finally, we propose a dense layer for answer retrieval. To evaluate our model, we used the ImageCLEF 2019 VQA data set. Our model outperforms existing visual question-and-answer systems and offers a significantly higher retrieval accuracy rate.
Hydrological time series prediction by extreme learning machine and sparrow search algorithm

Abstract In general, accurate hydrological time series prediction information is of great significance for the rational planning and management of water resource systems. Extreme learning machine (ELM) is an effective tool proposed for the single-layer feedforward neural network in regression and classification problems. However, the standard ELM model falls into a local minimum with a high probability in hydrological prediction problems since the randomly assigned parameters (like input-hidden weights and hidden biases) often remain unchanged in the learning process. For effectively improving the prediction accuracy, this paper develops a hybrid hydrological forecasting model where the emerging sparrow search algorithm (SSA) is firstly used to determine the satisfying parameter combinations of the ELM model, and then the Moore–Penrose generalized inverse method is chosen to analytically obtain the weight matrix between the hidden layer and output layer. The proposed method is used to forecast the long-term daily runoff series collected from three real-world hydrological stations in China. Based on several performance evaluation indexes, the results show that the proposed method outperforms several ELM variants optimized by other evolutionary algorithms in both training and testing phases. Hence, an effective evolutionary machine-learning tool is developed for accurate hydrological time series forecasting.
Multilayer encoder and single-layer decoder for abstractive Arabic text summarization

In this paper, an abstractive Arabic text summarization model that is based on sequence-to-sequence recurrent neural networks is proposed. It consists of a multilayer encoder and single-layer decoder. Encoder layers utilize bidirectional long short-term memory, whereas the decoder utilizes unidirectional long short-term memory. The encoder layers are the input text layer, keywords layer and the name entities layer. Moreover, the decoder uses a global attention mechanism that considers all the input hidden states to generate the summary words. The experiments are conducted on a dataset collected from several resources. The quality of the generated summary is measured quantitatively and qualitatively. In the quantitative measure, in addition to ROUGE1, three new evaluation measures are proposed to evaluate the quality of the generated summary, called ROUGE1-NOORDER, ROUGE1-STEM and ROUGE1-CONTEXT. One of the reasons for proposing new evaluation measures is that the abstractive nature of the summary requires more context based evaluations. Another reason refers to the morphological nature of the Arabic language since several words can be generated from the same root using morphemes. Moreover, a qualitative evaluation measure that is performed by a human is used to evaluate the readability and the relevance of the generated summary since it is hard to automatically measure the readability and relevance. The experimental results show that the multilayer encoder models provide the best results, where the values of ROUGE1, ROUGE1-NOORDER, ROUGE1-STEM and ROUGE1-CONTEXT of the proposed model are 38.4, 46.2, 52.6 and 58.1, respectively. Furthermore, the qualitative evaluation shows that the proposed model is the best, achieving an average readability and relevant value equal to 75.9%.
Communication of nutrition information by influencers on social media: A scoping review

Abstract Issue addressed Although government promoted public health, social media and media campaigns have communicated nutrition information, the Australian population remains chronic under‐consumers of fruit and vegetables and overconsumers of ultra‐processed foods. This scoping review aimed to determine how social media influencers (SMI) communicate nutrition information and the factors that influence the popularity of messages. Identified factors could inform how governments may utilise social media to impact positively on food choices. Methods Nine databases were searched in the past 5 years (2016‐2021). After relevant sources were identified, entire texts of the grey literature and the “Results” sections of the academic literature were coded. Using Microsoft Word, each key feature of the relevant text was highlighted, and the relevant code was recorded. Inductive coding was utilised where codes were created based on the text itself. These codes were then sorted iteratively into relevant themes and subthemes. Results Eleven studies were included. From these sources, five themes were identified. These were, (i) promoting dietary change, (ii) certain modes/styles of content delivery, (iii) an attractive individual, (iv) language features and (v) appearing connected to the audience. Conclusion SMI are selective in their content and communication techniques to ensure their nutrition messages are popular with social media users. These methods exhibited by SMI could inform strategies to build trust in government messages about food and nutrition.
Attention integrated hierarchical networks for no-reference image quality assessment

Quality assessment of natural images is influenced by perceptual mechanisms, e.g., attention and contrast sensitivity, and quality perception can be generated in a hierarchical process. This paper proposes an architecture of Attention Integrated Hierarchical Image Quality networks (AIHIQnet) for no-reference quality assessment. AIHIQnet consists of three components: general backbone network, perceptually guided neck network, and head network. Multi-scale features extracted from the backbone network are fused to simulate image quality perception in a hierarchical manner. The attention and contrast sensitivity mechanisms modelled by an attention module capture essential information for quality perception. Considering that image rescaling potentially affects perceived quality, appropriate pooling methods in the non-convolution layers in AIHIQnet are employed to accept images with arbitrary resolutions. Comprehensive experiments on publicly available databases demonstrate outstanding performance of AIHIQnet compared to state-of-the-art models. Ablation experiments were performed to investigate the variants of the proposed architecture and reveal importance of individual components.
Collision avoidance and path planning for industrial manipulator using slice-based heuristic fast marching tree

With the emergence of Industry 4.0, high productivity is critically dependent on robot manipulators. However, building an efficient and safe work environment with robot manipulators remains a challenge of hardware capability. The optimal path planning of the robot manipulator usually encounters shortcomings in low computational speed and tedious training after changing assembly lines and increases the risk during human–robot collaboration (HRC). To solve such a problem, we propose a path planning, named slice-based heuristic fast marching tree, based on joint space to achieve real-time path planning speed without modeling or training the workspace in advance. Our experimental results indicate that the time consumed for path planning in static environments is only from 0.51 s to 1.63 s, tested by a 6-DOF general-purpose industrial manipulator and different cylindrical obstacle placements. The time for path replanning in dynamic environments is from 0.62 s to 0.88 s.
Sub-Region Localized Hashing for Fine-Grained Image Retrieval

Fine-grained image hashing is challenging due to the difficulties of capturing discriminative local information to generate hash codes. On the one hand, existing methods usually extract local features with the dense attention mechanism by focusing on dense local regions, which cannot contain diverse local information for fine-grained hashing. On the other hand, hash codes of the same class suffer from large intra-class variation of fine-grained images. To address the above problems, this work proposes a novel sub-Region Localized Hashing (sRLH) to learn intra-class compact and inter-class separable hash codes that also contain diverse subtle local information for efficient fine-grained image retrieval. Specifically, to localize diverse local regions, a sub-region localization module is developed to learn discriminative local features by locating the peaks of non-overlap sub-regions in the feature map. Different from localizing dense local regions, these peaks can guide the sub-region localization module to capture multifarious local discriminative information by paying close attention to dispersive local regions. To mitigate intra-class variations, hash codes of the same class are enforced to approach one common binary center. Meanwhile, the gram-schmidt orthogonalization is performed on the binary centers to make the hash codes inter-class separable. Extensive experimental results on four widely used fine-grained image retrieval datasets demonstrate the superiority of sRLH to several state-of-the-art methods. The source code of sRLH will be released at https://github.com/ZhangYajie-NJUST/sRLH.git.
Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling

Training a machine learning algorithm from a class-imbalanced dataset is an inherently challenging task. The task becomes more challenging when compounded by high dimensionality (a high number of features). Feature extraction is a data reduction process that transforms features into linear or non-linear combinations of the original features, resulting in a smaller and richer set of attributes. Data sampling is a popular approach for addressing class imbalance. In this paper, our proposed method requires the implementation of feature extraction before data sampling, based on the idea that richer high-level features facilitate more efficient sampling and hence, produce better classification results. We use principal component analysis (PCA) and convolutional autoencoder (CAE) as the feature extraction techniques and synthetic minority oversampling technique (SMOTE) as the data sampling technique. In evaluating the performance of the random forest classifier on a credit card fraud dataset, our results show that CAE is a better feature extraction technique than PCA. The combination of CAE followed by SMOTE yields the best F1-score of 90.5%.
Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%.
Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.
Investigation and Research on the Negotiation Space of Mental and Mental Illness Based on Metaverse

Combining the current era background of the rapid development of related industries in metaverse and the social environment brought about by the COVID-19. As a result, there are more and more cases of receiving treatment for mental illness in the metaverse space. Therefore, in the current actual situation, the main body of this article is to use a game engine to build a high-realistic virtual diagnosis and treatment stage based on the metaverse. This project has two components: This project has two components: 1. Design a metaverse space scene. Designed to improve the discomfort of elderly users in the metaverse environment. 2. The PBR (Physically Based Rendering) rendering material processing method in the metaverse stage. This article aims to establish a digital diagnosis and treatment space based on the metaverse, which can help patients reduce the unfamiliarity of online diagnosis and treatment, provide a more active psychotherapy atmosphere, and reduce the discomfort of patients during the treatment process. Furthermore, based on metaverse, this article aims to build a more intelligent, realistic, and visualized digital therapy stage and the highly realistic performance of the digital therapy treatment content in the future under visualization.
BUAKA-CS: Blockchain-enabled user authentication and key agreement scheme for crowdsourcing system

Crowdsourcing is a practice of using collective intelligence of a group in order to achieve a common goal to solve complex problems in an innovative way. It involves obtaining information and opinions from a group of participants who submit their data (i.e., solutions) via the Internet using some applications. The application domains, where crowdsourcing can be used, include, but not limited to, healthcare, environment, public safety, disaster management, and transportation. Despite the unprecedented advantages of crowdsourcing, security and privacy are rising concerns that need to be addressed. Therefore, it is crucially important to provide effective solutions that address the security and privacy issues in crowdsourcing systems. To this end, the salient features of blockchain technology such as immutability, decentralization, transparency and resiliency can play a pivotal role to address the afore-mentioned security challenges. To fill these gaps, in this paper, we propose a new blockchain-based user authentication and key agreement scheme for crowdsourcing (BUAKA-CS) through lightweight cryptographic techniques. The security of the BUAKA-CS is proved through the formal method and also through other mathematical methods that depict the resilience of BUAKA-CS against various types of possible attacks. Moreover, the robustness of BUAKA-CS against possible attacks is proved through widely-recognized automated software validation tools. We also compare BUAKA-CS with other existing schemes and prove its out performance in terms of security, functionality, computation and communication costs. Finally, we conduct the extensive blockchain-based simulations to measure the impact of BUAKA-CS on the performance of the system.
Towards Continuous Deployment for Blockchain

Ensuring a production-ready state of the application under development is the immanent feature of the continuous delivery approach. In a blockchain network, nodes communicate, storing data in a decentralized manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research, focusing on continuous practices for blockchain and distributed ledger technology. In particular, such works with support for both software development disciplines of design and deployment. Artifacts from considered disciplines have been placed in the 1 + 5 architectural views model. The approach aims to ensure the continuous deployment of containerized blockchain distributed applications. The solution has been divided into two independent components: Delivery and deployment. They interact through Git distributed version control. Dedicated GitHub repositories should store the business application and deployment configurations for nodes. The delivery component has to ensure the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The deployment component is responsible for providing running distributed applications in containers for all blockchain nodes. The approach uses Jenkins and Kubernetes frameworks. For the sake of verification, preliminary tests have been conducted for the Electricity Consumption and Supply Management blockchain-based system for prosumers of renewable energy.