Reviewerly/exHarmony huggingface dataset:
Cloud–edge collaboration-based bi-level optimal scheduling for intelligent healthcare systems

The development of intelligent healthcare systems (IHS) has raised the added value of digital medical data. With an efficient exploitation of digital medical data for diagnosis assistance, federated learning (FL) is promising in future digital health care. However, in multiple task performances, federated nodes deployed at the edge of IHS are constrained by computing and storage resources, as well as increased privacy breach risks. On account of these challenges, this paper proposes a more elaborated cloud–edge collaboration (CEC) framework of IHS combining FL and blockchain. Thus, a bi-level optimization scheduling IHS model is proposed, considering the large-scale access requirement of distributed generation (DG), energy storage (ES) and controllable load (CL) access to the IHS. Simulation results confirm an effective reduction of execution delay and power consumption, and a better interest coordination among multi-stakeholders.
Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification

Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.
Prediction of Prospecting Target Based on ResNet Convolutional Neural Network

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">G</mml:mi></mml:math>-LIME: Statistical learning for local interpretations of deep neural networks using global priors

To explain the prediction result of a Deep Neural Network (DNN) model based on a given sample, LIME [1] and its derivatives have been proposed to approximate the local behavior of the DNN model around the data point via linear surrogates. Though these algorithms interpret the DNN by finding the key features used for classification, the random interpolations used by LIME would perturb the explanation result and cause the instability and inconsistency between repetitions of LIME computations. To tackle this issue, we propose G-LIME that extends the vanilla LIME through high-dimensional Bayesian linear regression using the sparsity and informative global priors. Specifically, with a dataset representing the population of samples (e.g., the training set), G-LIME first pursues the global explanation of the DNN model using the whole dataset. Then, with a new data point, G-LIME incorporates an modified estimator of ElasticNet-alike to refine the local explanation result through balancing the distance to the global explanation and the sparsity/feature selection in the explanation. Finally, G-LIME uses Least Angle Regression (LARS) and retrieves the solution path of a modified ElasticNet under varying ℓ1-regularization, to screen and rank the importance of features [2] as the explanation result. Through extensive experiments on real world tasks, we show that the proposed method yields more stable, consistent, and accurate results compared to LIME.
An efficient multi-robot path planning solution using A* and coevolutionary algorithms

Multi-robot path planning has evolved from research to real applications in warehouses and other domains; the knowledge on this topic is reflected in the large amount of related research published in recent years on international journals. The main focus of existing research relates to the generation of efficient routes, relying the collision detection to the local sensory system and creating a solution based on local search methods. This approach implies the robots having a good sensory system and also the computation capabilities to take decisions on the fly. In some controlled environments, such as virtual labs or industrial plants, these restrictions overtake the actual needs as simpler robots are sufficient. Therefore, the multi-robot path planning must solve the collisions beforehand. This study focuses on the generation of efficient collision-free multi-robot path planning solutions for such controlled environments, extending our previous research. The proposal combines the optimization capabilities of the A* algorithm with the search capabilities of co-evolutionary algorithms. The outcome is a set of routes, either from A* or from the co-evolutionary process, that are collision-free; this set is generated in real-time and makes its implementation on edge-computing devices feasible. Although further research is needed to reduce the computational time, the computational experiments performed in this study confirm a good performance of the proposed approach in solving complex cases where well-known alternatives, such as M* or WHCA, fail in finding suitable solutions.
Usability, user experience and mental workload in a mobile Augmented Reality application for digital storytelling in cultural heritage

Abstract Augmented Reality (AR) has become an increasingly used technology to support and enhance the enjoyment of cultural heritage. Particularly relevant is its importance for digital storytelling: by framing a portion of a fresco or painting with a smartphone, an AR mobile application can provide contextually relevant information, also in the form of multimedia content, that can help the user to understand the story and meaning behind the images. In this type of application, human factors are of fundamental importance for the effectiveness of the narrative: a mobile AR application must avoid distracting the user’s attention from the content in order to encourage a good level of concentration and immersion. The case study presented in this paper deals with a mobile AR application developed to guide visitors in the interpretation of the frescoes inside the Basilica of Saint Catherina of Alexandria in Galatina. The aim of the study is the analysis of the relations among usability, user experience and mental workload factors in AR-based digital storytelling.
Augmented Reality Applications for Learning Geography in Primary Education

Augmented Reality is an emerging educational technology that has the potential to provide innovative methods of teaching and create engaging learning experiences. Augmented Reality applications implementing game-based design could enrich education by increasing motivation and engagement and enabling better learning outcomes. Similarly, Augmented Reality, in the context of Geography, could enhance the learning process and the user experience through the visualization of the content and a better understanding of abstract concepts. Therefore, in this study, (a) three specially designed Augmented Reality applications are described for teaching Geography in the fifth and sixth grades, and (b) an extensive usability evaluation study is reported using the three applications. Teachers (N = 6) and pupils (N = 43) from the fifth and sixth grades, as well as computer science students (N = 43) participated to assess the usability of the proposed Augmented Reality apps. The results were positive since the proposed Augmented Reality apps provided high-level usability. Finally, they revealed that there was acceptance for the Augmented Reality technology by all participants and a willingness to be incorporated into the teaching process.
Combination of visuo-tactile and visuo-vestibular correlations in illusory body ownership and self-motion sensations

Previous studies have shown that illusory ownership over a mannequin's body can be induced through synchronous visuo-tactile stimulation as well as through synchronous visuo-vestibular stimulation. The current study aimed to elucidate how three-way combinations of correlated visual, tactile and vestibular signals contribute to the senses of body ownership and self-motion. Visuo-tactile temporal congruence was manipulated by touching the mannequin's body and the participant's unseen real body on the trunk with a small object either synchronously or asynchronously. Visuo-vestibular temporal congruence was manipulated by synchronous or asynchronous presentation of a visual motion cue (the background rotating around the mannequin in one direction) and galvanic stimulation of the vestibular nerve generating a rotation sensation (in the same direction). The illusory experiences were quantified using a questionnaire; threat-evoked skin-conductance responses (SCRs) provided complementary indirect physiological evidence for the illusion. Ratings on the illusion questionnaire statement showed significant main effects of synchronous visuo-vestibular and synchronous visuo-tactile stimulations, suggesting that both of these pairs of bimodal correlations contribute to the ownership illusion. Interestingly, visuo-tactile synchrony dominated because synchronous visuo-tactile stimulation combined with asynchronous visuo-vestibular stimulation elicited a body ownership illusion of similar strength as when both bimodal combinations were synchronous. Moreover, both visuo-tactile and visuo-vestibular synchrony were associated with enhanced self-motion perception; self-motion sensations were even triggered when visuo-tactile synchrony was combined with visuo-vestibular asynchrony, suggesting that ownership enhanced the relevance of visual information as a self-motion cue. Finally, the SCR results suggest that synchronous stimulation of either modality pair led to a stronger illusion compared to the asynchronous conditions. Collectively, the results suggest that visuo-tactile temporal correlations have a stronger influence on body ownership than visuo-vestibular correlations and that ownership boosts self-motion perception. We present a Bayesian causal inference model that can explain how visuo-vestibular and visuo-tactile information are combined in multisensory own-body perception.
Towards understanding and mitigating unintended biases in language model-driven conversational recommendation

Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are prone to intrinsic biases in their training data, which may be exacerbated by biases embedded in domain-specific language data (e.g., user reviews) used to fine-tune LMs for CRSs. We study a simple LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias — i.e., bias due to language variations such as name references or indirect indicators of sexual orientation or location that should not affect recommendations — manifests in substantially shifted price and category distributions of restaurant recommendations. For example, offhand mention of names associated with the black community substantially lowers the price distribution of recommended restaurants, while offhand mentions of common male-associated names lead to an increase in recommended alcohol-serving establishments. While these results raise red flags regarding a range of previously undocumented unintended biases that can occur in LM-driven CRSs, there is fortunately a silver lining: we show that train side masking and test side neutralization of non-preferential entities nullifies the observed biases without significantly impacting recommendation performance.
Does A Secondary Task Inhibit Vection in Virtual Reality?

Vection is commonly defined as the illusory sensation of self-motion. Research on vection can assist in improving the fidelity of motion simulators. Vection can be influenced through top-down factors, such as attention, but previous research on the effect of a secondary task on vection presented conflicting findings. We investigated the effect of a visual discrimination reaction time task on vection. Twenty-nine participants were visually and audibly immersed in virtual environments with different levels of ecological relevance wherein they used a joystick to continuously report on their vection experience. In contrast to previous research, our results showed no significant effect of a secondary task on vection measures nor an effect of sensory cues and environment context on secondary task performance. We conclude that participants’ ability to report their vection experience was unaffected whilst performing a visual attention reaction time task.
Efficient Evolving Deep Ensemble Medical Image Captioning Network

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate. Therefore, in this paper, to provide explanatory information to the doctors and patients, an efficient deep ensemble medical image captioning network (DCNet) is proposed. DCNet ensembles three well-known pre-trained models such as VGG16, ResNet152V2, and DenseNet201. Ensembling of these models achieves better results by preventing an over-fitting problem. However, DCNet is sensitive to its control parameters. Thus, to tune the control parameters, an evolving DCNet (EDC-Net) was proposed. Evolution process is achieved using the self-adaptive parameter control-based differential evolution (SAPCDE). Experimental results show that EDC-Net can efficiently extract the potential features of biomedical images. Comparative analysis shows that on the Open-i dataset, EDC-Net outperforms the existing models in terms of BLUE-1, BLUE-2, BLUE-3, BLUE-4, and kappa statistics (KS) by 1.258%, 1.185%, 1.289%, 1.098%, and 1.548%, respectively.
Blockchain-Based Secure Authentication with Improved Performance for Fog Computing

Advancement in the Internet of Things (IoT) and cloud computing has escalated the number of connected edge devices in a smart city environment. Having billions more devices has contributed to security concerns, and an attack-proof authentication mechanism is the need of the hour to sustain the IoT environment. Securing all devices could be a huge task and require lots of computational power, and can be a bottleneck for devices with fewer computational resources. To improve the authentication mechanism, many researchers have proposed decentralized applications such as blockchain technology for securing fog and IoT environments. Ethereum is considered a popular blockchain platform and is used by researchers to implement the authentication mechanism due to its programable smart contract. In this research, we proposed a secure authentication mechanism with improved performance. Neo blockchain is a platform that has properties that can provide improved security and faster execution. The research utilizes the intrinsic properties of Neo blockchain to develop a secure authentication mechanism. The proposed authentication mechanism is compared with the existing algorithms and shows that the proposed mechanism is 20 to 90 per cent faster in execution time and has over 30 to 70 per cent decrease in registration and authentication when compared to existing methods.
Hierarchical multi-label taxonomic classification of carbonate skeletal grains with deep learning

Recent advances in Artificial Intelligence (AI), particularly the rise of deep learning, are revolutionizing data collection and analysis in many aspects of the Earth Sciences, including paleontology. Rapid digital transformation of paleontological information enables automation of various paleontological tasks using AI technology. One such task is identifying and classifying skeletal grains at different levels of the Linnaean taxonomic hierarchy, both macro- and microscopically. Unfortunately, fossil classification remains largely untouched by AI due to the expertise and time required to generate high-quality, large-scale labeled training data. This task is particularly challenging when part of the compositional analysis of limestone, where three-dimensional objects (i.e., skeletal grains) are observed in a two-dimensional cross-section. It is compounded by the imbalanced data and limited number of images available, approximately four orders smaller than the number necessary to train deep neural networks. Recent efforts to classify such fossil images using deep learning returned a single output/label classification, limiting the information output from the model, and hindering its further applications to replicate paleontologist-level identification. Here, we couple a multi-head deep convolutional neural network architecture called TaxonNet that adopts the Branch Convolutional Neural Networks and advanced image augmentation to overcome these barriers. This model uses prior knowledge of hierarchical category relationships to output multiple hierarchical taxonomic predictions from a single petrographic image. In this study, we developed and evaluated four different strategies of hierarchical classification: (i) forward (coarse to fine); (ii) reverse (fine to coarse); (iii) parallel (coarse and fine simultaneously), and (iv) hybrid. The forward and reverse strategies yield the most accurate prediction of ~95 % accuracy for coarse-level and fine-level recognitions across major fossil types in carbonate petrographic images, including algae, mollusks, and corals. These results represent an important step toward applying AI to advance fossil grain classification and exhibit a promising real-world geological application of a hierarchical network in paleontology, sedimentary petrography, and other image-based geological analysis requiring hierarchical, multilabel output.
A node selection algorithm with a genetic method based on PBFT in consortium blockchains

Abstract Industry and research communities have widely studied Blockchain technology, and the consortium blockchain is currently the most used category with a wide range of applications. However, issues, such as the performance of consensus mechanisms, have become essential constraints on promoting and applying the consortium blockchain. To improve the performance of the consortium blockchain consensus, we use the practical Byzantine fault tolerance (PBFT) consensus widely used in consortium blockchains to reduce the number of consensus nodes to optimize performance. Using the PBFT consensus, we screen high-performance nodes and obtain a reliable and limited number of consensus nodes. We propose a genetic algorithm-based blockchain consensus algorithm improvement scheme, design the fitness function of blockchain nodes and the genetic algorithm to iterate out consensus node groups with excellent indicators continuously, and finally iterate the nodes participating in the consensus. This algorithm can increase the speed and efficiency of the consensus, block generation, and computation. The algorithm in this article is tested on the FISCO BCOS (i.e., a consortium blockchain platform built by the FISCO open-source working group), and controlled experiments and the experimental results illustrate the safety and practicability of the method.
Exploring User Behaviour in Asymmetric Collaborative Mixed Reality

A common issue for collaborative mixed reality is the asymmetry of interaction with the shared virtual environment. For example, an augmented reality (AR) user might use one type of head-mounted display (HMD) in a physical environment, while a virtual reality (VR) user might wear a different type of HMD and see a virtual model of that physical environment. To explore the effects of such asymmetric interfaces on collaboration we present a study that investigates the behaviour of dyads performing a word puzzle task where one uses AR and the other VR. We examined the collaborative process through questionnaires and behavioural measures based on positional and audio data. We identified relationships between presence and co-presence, accord and co-presence, leadership and talkativeness, head rotation velocity and leadership, and head rotation velocity and talkativeness. We did not find that AR or VR biased subjective responses, though there were interesting behavioural differences: AR users spoke more words, AR users had a higher median head rotation velocity, and VR users travelled further.