Reviewerly/exHarmony huggingface dataset:
Medical Healthcare System with Hybrid Block based Predictive models for Quality preserving in Medical Images using Machine Learning Techniques

Cloud technology is a business strategy that aims to provide the necessary material to customers depending on their needs. Individuals and cloud businesses alike have embraced the cloud storage service, which has become the most widely used service. The industries outsource their data to cloud storage space to relieve themselves of a load of dealing with redundant data contents. This must be protected to prevent the theft of personal belongings, and privacy must be improved as well. Different research projects have been suggested to ensure the safe management of the information included within the data content. The security of current research projects, on the other hand, still needs improvement. As a result, this method has been suggested to address the security concerns associated with cloud computing. The primary goal of this study effort is to offer a safe environment for cloud users while also increasing the profit of cloud resource providers by managing and securely delivering data contents to the cloud users. The bulk of sectors, including business, finance, the military, and the healthcare industry, do not store data in cloud-based storage systems. This technique is used to attract these kinds of customers. Increasing public acceptance Medical researchers are drawn to cloud computing because it allows them to store their study material in a centralized location and distribute and access it in a more flexible manner. They were collected from numerous individuals who were being evaluated for medical care at the time. Scalable and Enhanced Key Aggregate Cryptosystem is a protected data protection method that provides highly effective security in the health care industry. This approach handles disagreements in the outflow of sensitive information and guarantees the data security deployment of a Cloud-based Intelligent Health Monitoring system for the parties involved in the dispute. Using the suggested method, the encrypted data format of medical and health-care prescriptions is recorded as it passes through the hands of patients and healthcare institutions. To increase the level of security, the double encryption method is used. During the encryption process, the Ciphertext ID is referred to as a class. The keyholder is a master secret key that aids in the retrieval of the secret keys of different kinds of monsters and creatures. The extracted key is transmitted and kept as a single aggregate for the benefit of the patient or client to facilitate decryption. Between the use of a key aggregation cryptosystem and double encryption method, the Cloud-based Intelligent Health Monitoring systems may establish a secure link with Healthcare Organizations and patients. As a result, when compared to prior methods, the results demonstrate that the study methodology achieves high levels of security in terms of confidentiality and integrity, as well as great scalability.
Deep learning approaches in flow visualization

Abstract With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning.
Crypto and digital currencies — nine research priorities

To avert privacy breaches, scams and environmental damage, governments and central banks need to know how best to regulate this financial frontier. To avert privacy breaches, scams and environmental damage, governments and central banks need to know how best to regulate this financial frontier.
An adaptive access control scheme based on trust degrees for edge computing

Access control is an essential mechanism in collaborative environments, which guarantees the security of shared resources. With the intertwinement of emerging technologies likes edge computing, cloud, Artificial Intelligence (AI), and Internet of Things (IoT), access control will encounter its innovation and development shortly. Whereas, traditional access control did not consider the adaptive and fine-grained requirements in edge computing. In addition, the integrity of the accessed data is not considered in the traditional access control scheme. In this paper, inspired by the technologies in AI and cloud, an adaptive access control scheme based on trust degrees is proposed. The trust degrees of users and the quality of data are defined. On this basis, the trust degrees-based adaptive access control framework is proposed. Moreover, in the proposed scheme, technologies in AI and cryptography are converged. In particular, the ciphertext policy attribute-based encryption (CP-ABE) is integrated with the classification and recommendation algorithms in AI, thereby providing adaptive and fine-grained properties for access control in edge computing. Moreover, the integrity of the accessed data is guaranteed by the auditing technique in cryptography. Finally, the proposed access control scheme is implemented by using the PBC library. For supporting the efficient computation in edge computing, all types of elliptic curves provided by the PBC library are tested. The experimental result shows that Type D curve is the most efficient in implementing the proposed scheme.
A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems

Recently, autonomous systems have received considerable attention amongst research communities and academicians. Unmanned aerial vehicles (UAVs) find useful in several applications like transportation, surveillance, disaster management, and wildlife monitoring. One of the important issues in the UAV system is energy efficiency, which can be resolved by the use of clustering approaches. In addition, high resolution remote sensing images need to be classified for effective decision making using deep learning (DL) models. Though several models are available in the literature, only few approaches have focused on the clustering and classification processes in UAV networks. In this aspect, this paper designs a novel metaheuristic with an adaptive neuro-fuzzy inference system for decision making named MANFIS-DM technique on autonomous UAV systems. The proposed MANFIS-DM technique intends to effectively organize the UAV networks into clusters and then classify the images into appropriate class labels. The proposed MANFIS-DM technique encompasses two major stages namely quantum different evolution based clustering (QDE-C) technique and ANFIS based classification technique. Primarily, the QDE-C technique involves the design of a fitness function involving three parameters namely average distance, distance to UAVs, and UAV degree. Besides, the image classification model involves a set of subprocesses namely DenseNet based feature extraction, Adadelta based hyperparameter optimization, and ANFIS based classification. The design of QDE-C algorithm with classification model for autonomous UAV systems show the novelty of the work. The experimental result analysis of the MANFIS-DM method is carried out against benchmark dataset and the results ensured the enhanced performance of the MANFIS-DM technique over the other methods with the maximum accuy of 99.13%.
An Augmented Reality Based Mobile Photography Application to Improve Learning Gain, Decrease Cognitive Load, and Achieve Better Emotional State

Taking photos by mobile phones has become an indispensable part of people's daily life with the popularity of smartphones. However professional photography content is also needed for taking high-quality photos by mobile phones as ones taken by professional cameras. Existing learning tools for photography can't provide photographic practice while presenting theoretical content, nor give real-time photography guidance according to the environment. Mobile augmented reality (AR) technology provides a new way to solve these problems by creating a real learning situation. Therefore, an innovative AR-based mobile photography application (ARMPA) is designed and developed as a method to connect theory and practice. Based on Learning-by-Doing instructional strategy, the ARMPA can timely and synchronously display the theoretical content of photography in the form of text and images corresponding to learners' practical operation. Moreover, it can provide learners real-time interaction and guidance by using AR environment sensing technology. The effect of ARMPA on learning gain and cognitive load is assessed by traditional methods of pre/post-test and questionnaires. To objectively explore the effectiveness of ARMPA from a cognitive and emotional perspective, we measure and extract four emotional state indexes of electroencephalogram (EEG) including engagement (ENG), relaxation (MED), interest (INT) and focus (FOC). Compared to traditional two-dimension (2 D) application (APP), ARMPA improves learners' learning gains, decreases learners' cognitive loads. The participants in the AR group achieve better emotional states than those in the 2 D group. This study provides some empirical evidence for AR technology to boost mobile phone photography.
The impact of eye-tracking games as a training case on students' learning interest and continuous learning intention in game design courses: Taking Flappy Bird as an example

This paper explores the learning effect of introducing eye-tracking games in college game design courses and tests the impact of innovative introduction of cutting-edge interactive content on students' learning in higher education in the field of digital media. The results show that a stronger perceived interactivity is linked to stronger user perceived control and more positive flow experience. Furthermore, due to the higher perceived interactivity brought by eye-tracking games than traditional games, the improvement of flow experience after teaching also brings obvious improvement of students' learning interest and continuous learning intention. It is worth noting that this effect seems to have a more robust gain effect in the group of sophomores, while the effect shows a transient characteristic in the group of juniors. The difference may be related to students' learning ability. At the same time, three main factors for the positive influence of eye-tracking games on teaching were extracted, including 'Technology Sensitive', 'Operational' and 'adventures by Difference'. Three main factors for the negative effects were also found, including 'Unattractive Game Types', 'Self-caused Resistance' and 'Game Maturity Deficiency'. The results of the study explored how to amplify the help of eye-tracking games in the teaching process and reduce the possible drawbacks from the extracted multiple constructs. Therefore, as an innovative teaching case with strong interaction, the adoption of eye-tracking game learning is indeed helpful to the course of digital media major.
A sparse graph wavelet convolution neural network for video-based person re-identification

Video-based person re-identification (Re-ID) aims to match identical person sequences captured across non-overlapping surveillance areas. It is an essential yet challenging task to effectively embed spatial and temporal information into the video feature representation. For one thing, we observe that different frames in the video can provide complementary information for each other. Also, local features which is lost due to target occlusion or visual ambiguity in one frame can be supplemented by the same pedestrian part in other frames. For another thing, graph neural network enables the contextual interactions between relevant regional features. Therefore, we propose a novel sparse graph wavelet convolution neural network (SGWCNN) for video-based person Re-ID. Distinct from previous graph-based Re-ID methods, we exploit the weighted sparse graph to model the semantic relation among the local patches of pedestrians in the video. Each local patch in one frame can extract supplementary information from highly related patches in other frames. Moreover, to effectively solve the problems of short time occlusion and pedestrian misalignment, the graph wavelet convolution neural network is adopted for feature propagation to refine regional features iteratively. Experiments and evaluation on three challenging benchmarks, that is, MARS, DukeMTMC-VideoReID, and iLIDS-VID, show that the proposed SGWCNN effectively improves the performance of video-based person re-identification.
An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis

Pulmonary hypertension (PH) is a rare and fatal condition that leads to right heart failure and death. The pathophysiology of PH and potential therapeutic approaches are yet unknown. PH animal models' development and proper evaluation are critical to PH research. This work presents an effective analysis technology for PH from arterial blood gas analysis utilizing an evolutionary kernel extreme learning machine with multiple strategies integrated slime mould algorithm (MSSMA). In MSSMA, two efficient bee-foraging learning operators are added to the original slime mould algorithm, ensuring a suitable trade-off between intensity and diversity. The proposed MSSMA is evaluated on thirty IEEE benchmarks and the statistical results show that the search performance of the MSSMA is significantly improved. The MSSMA is utilised to develop a kernel extreme learning machine (MSSMA-KELM) on PH from arterial blood gas analysis. Comprehensively, the proposed MSSMA-KELM can be used as an effective analysis technology for PH from arterial Blood gas analysis with an accuracy of 93.31%, Matthews coefficient of 90.13%, Sensitivity of 91.12%, and Specificity of 90.73%. MSSMA-KELM can be treated as an effective approach for evaluating mouse PH models.
Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.
A genetic algorithm for the close-enough traveling salesman problem with application to solar panels diagnostic reconnaissance

This paper addresses a variant of the classical Traveling Salesman Problem known as Close-Enough Traveling Salesman Problem . In this problem, there is a set of nodes (customers, targets), each of them associated with a region, denoted as neighborhood, that contains it. The goal is to determine the shortest tour that visits all the nodes, where a node is visited when the tour traverses or reaches the region associated with the node. We propose a genetic algorithm (GA), which uses several strategies to optimize the tour, such as 2opt, second-order cone programming, and a bisection algorithm. The proposed approach is tested on 62 benchmark instances. The results show that GA produces better or similar solutions compared to the ones produced by state-of-the-art algorithms in a reasonable computing time. Besides, GA found 32 new best solutions out of 62 instances. Furthermore, we propose different metrics to classify problem instances with the goal of detecting which problem characteristics have a larger impact on the difficulty of solving the problem. We also revised the already proposed metric, called Overlap Ratio , and correct its calculation done in previous contributions. Finally, we present a case study related to the diagnostic reconnaissance of solar panels. The case study is related to a research project developed at the University of Molise in collaboration with private IT companies. We show that the problem under study is properly modeled as a Close-Enough Traveling Salesman Problem and apply the GA to solve it, focusing on the benefits obtained by applying this solution approach. • Metaheuristic approach that provides better solutions for CETSP instances. • Novel metrics to estimate instances complexity. • Revised overlap ratio values. • Case study related to the diagnostic reconnaissance of solar panels.
Consumer Consciousness in Multisensory Extended Reality

The reality-virtuality continuum encompasses a multitude of objects, events and environments ranging from real-world multisensory inputs to interactive multisensory virtual simulators, in which sensory integration can involve very different combinations of both physical and digital inputs. These different ways of stimulating the senses can affect the consumer's consciousness, potentially altering their judgements and behaviours. In this perspective paper, we explore how technologies such as Augmented Reality (AR) and Virtual Reality (VR) can, by generating and modifying the human sensorium, act on consumer consciousness. We discuss the potential impact of this altered consciousness for consumer behaviour while, at the same time, considering how it may pave the way for further research.
A cluster-based cooperative computation offloading scheme for C-V2X networks

The explosive growth of computation-intensive and delay-sensitive applications in vehicular edge network demands high computation resources and energy. With the cellular-vehicle to everything (C-V2X) technology, these applications can be transferred to another node for processing. However, offloading all these tasks to the mobile edge computing (MEC) server becomes a bottleneck. A cluster-based cooperative task offloading scheme for C-V2X networks is proposed. The proposed scheme explores the collaboration between the MEC servers and vehicles with idle computation resources known as the vehicular edge computing (VEC) servers. Vehicles can utilize the direct sidelink vehicle to vehicle (V2V) communication for task offloading to the VEC server and the cellular vehicle to network (V2N) communication for task offloading to the MEC server. In the proposed scheme, vehicles are grouped to form clusters in order to enhance link reliability between nodes for efficient task offloading. We then formulate an optimization problem to minimize energy consumption and task offloading latency, which is NP-hard. Accordingly, an improved matching algorithm is proposed to solve this problem by optimizing offloading decisions and resource allocation. According to our results, the proposed scheme reduces offloading latency and energy consumption compared to other existing schemes.
Quantifying relevance in learning and inference

Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data and Big Data promise to open new windows on complex systems such as cells, the brain or our societies. Yet, the puzzling success of Artificial Intelligence and Machine Learning shows that we still have a poor conceptual understanding of learning. These applications push statistical inference into uncharted territories where data is high-dimensional and scarce, and prior information on "true" models is scant if not totally absent. Here we review recent progress on understanding learning, based on the notion of "relevance". The relevance, as we define it here, quantifies the amount of information that a dataset or the internal representation of a learning machine contains on the generative model of the data. This allows us to define maximally informative samples, on one hand, and optimal learning machines on the other. These are ideal limits of samples and of machines, that contain the maximal amount of information about the unknown generative process, at a given resolution (or level of compression). Both ideal limits exhibit critical features in the statistical sense: Maximally informative samples are characterised by a power-law frequency distribution (statistical criticality) and optimal learning machines by an anomalously large susceptibility. The trade-off between resolution (i.e. compression) and relevance distinguishes the regime of noisy representations from that of lossy compression. These are separated by a special point characterised by Zipf's law statistics. This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance. Criticality in optimal learning machines manifests in an exponential degeneracy of energy levels, that leads to unusual thermodynamic properties.