Reviewerly/exHarmony huggingface dataset:
Nodes clustering and multi-hop routing protocol optimization using hybrid chimp optimization and hunger games search algorithms for sustainable energy efficient underwater wireless sensor networks

Clustering and routing processes in underwater wireless sensor networks (UWSNs) are challenging tasks in the underwater environment due to the multiplicity of sensor nodes, transmission bandwidth, and limited energy resources. In order to address the shortcomings mentioned above, this paper proposes a novel hybrid Chimp Optimization and Hunger Games Search (ChOA-HGS) algorithms for clustering and multi-hop routing optimization in UWSNs. In this approach, first, the ChOA is used to choose cluster heads and efficiently structure clusters. Then, the HGS-based routing procedure is used to determine the network's best pathways. The proposed approach combines the advantages of clustering and routing, resulting in optimal network lifetime and energy efficiency. The proposed ChOA-HGS is validated using a variety of measures after it is simulated using three different scenarios. In order to evaluate the performance of the ChOA-HGS, results are compared to PSO, MPSO, IPSO-GWO, TEEN, and LEACH. The results show that the ChOA-HGS outperformed other benchmarks in terms of lifetime and energy consumption.
Fine-grained interactive attention learning for semi-supervised white blood cell classification

White blood cell (WBC) is an essential part of the human immune system. To diagnose blood diseases, hematologists have to think about the WBC information. For instance, the number of each type of WBCs often implies the health condition of the human body. Thus, the classification of white blood cell images plays a significant role in the medical diagnosis process. However, manual WBC inspection is time-consuming and labor-intensive for experts, which means automated classification methods are needed for WBC recognition. Another problem is that the traditional automatic recognition system needs a large amount of annotated medical images for training, which is highly costly. In this respect, the semi-supervised learning framework has recently been widely used for medical diagnosis due to its specificity, which can explore relevant information from massive unlabeled data. In this study, a novel semi-supervised white blood cell classification method is proposed, named by Fine-grained Interactive Attention Learning (FIAL). It consists of a Semi-Supervised Teacher-Student (SSTS) module and a Fine-Grained Interactive Attention (FGIA) mechanism. In detail, SSTS employs limited labeled WBC images and generates predicted probability vectors for a large amount of unlabeled WBC samples, like a human. After top-k selection in predicted probabilities, the efficient data can be exploited from unlabeled WBC images for training. With a very small amount of annotated WBC images, FIAL achieves an average accuracy of 93.2% on BCCD dataset when giving 75 labeled images for each category, which sufficiently elaborates our excellent capability on semi-supervised white blood cell image classification task.
Virtual Reality in the Treatment of Patients with Overweight and Obesity: A Systematic Review

Obesity is one of the world’s most serious health issues. Therefore, therapists have looked for methods to fight obesity. Currently, technology-based intervention options in medical settings are very common. One such technology is virtual reality (VR) which has been used in the treatment of obesity since the late 1990s. The main objective of this study is to review the literature on the use of VR in the treatment of obesity and overweight to better understand the role of VR-based interventions in this field. To this end, four databases (PubMed, Medline, Scopus, and Web of Science) were searched for related publications from 2000 to 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 645 articles identified, 24 were selected. The main strength of this study is that it is the first systematic review to focus completely on the use of VR in the treatment of obesity. It includes most research in which VR was utilized to carry out the intervention. Although several limitations were detected in the reviewed studies, the findings of this review suggest that employing VR for self-monitoring of diet, physical activity, and/or weight is effective in supporting weight loss as well as improving satisfaction of body image and promoting health self-efficacy in overweight or obese persons.
Cryptocurrency price discrepancies under uncertainty: Evidence from COVID-19 and lockdown nexus

The past decades have witnessed recurrent price discrepancies in cryptocurrency markets across countries. In addition to prior explanations that generally attribute this phenomenon to domestic capital controls during normal periods, we provide another explanation that investors perceive cryptocurrency as an alternative (hedging) investment, especially under uncertainty. Using the emerging of the COVID-19 pandemic in 2020 and the subsequent lockdown policies implemented by a group of countries as natural experiments, we adopt a difference-in-difference framework to examine how the nexus affects Bitcoin price discrepancies. We find that price discrepancies are larger in countries with confirmed cases of COVID-19 and rigorously implementing lockdown policies. We then verify our "alternative investment" hypothesis on the mechanism by showing that countries with intensified exposure to media hype on COVID-19 topics and with more panic emotion among citizens during the pandemic generally experienced larger Bitcoin price discrepancies than their counterparts. We also find that domestic capital control, sanitary policy stringency, uncertainty aversion, individualistic culture, and governmental power could moderate the general effect.
Data-driven multi-index overlay gold prospectivity mapping using geophysical and remote sensing datasets

The comparative relevance of each geospatial component of mineralization differs from one geological terrane to the other because various sought-after mineral deposit-types synonymously differ in different geological terranes. Hence, the possibility of employing a conceptual model to obtain a relationship or a quantitative function between various geospatial features (evidential layers) with respect to the mineral being sought is laudable, though these features may not necessarily have a generically related effect with the mineral being sought. As a consequence, there is the need to employ a technique that has the capacity to recognize the efficient and inefficient geospatial indicators of the mineral deposit-type being sought. In view of this, this study employed the logistic function, concentration-area fractal model and the prediction-area (P-A) plot to transform and discretize the continuous value of each evidential layer as well as generating intersection points of prediction rate indicators that are essential in obtaining the normalized densities, which were subsequently employed in generating the objective weight for each evidential layer in a data-driven way. The P-A and the normalized density techniques employed were vital in recognizing the indicator and non-indicator criteria. The results obtained acknowledged the potassium concentration layer as a non-indicator of gold mineralization within the study area and subsequently recognized the hydroxyl bearing mineral concentration layer as the most plausible indicator criteria among the six evidential layers (lineament density, iron concentration, hydroxyl concentration, gravity anomaly, magnetic anomaly and potassium concentration) employed in this study. These five indicator criteria were integrated to generate a mineral prospectivity map (MPM) over the study area based on the data-driven multi-index overlay approach adopted. The prediction rate for each of the 6 evidential layers (5 of which were the indicator criteria) as well as the MPM produced indicates that, the generation of objective weights in a data-driven manner via normalized density enhances the predicting ability of the MPM produced in comparison with the individual evidential layers.
DAG blockchain-based lightweight authentication and authorization scheme for IoT devices

As the extension and expansion of the Internet, the Internet of things (IoT) connects things with the network through information sensing equipment to achieve intelligent identification, positioning, monitoring, management and other functions. With the increasing number of IoT users and devices, the demands for secure communication and data confidentiality are getting higher and higher, and the identity authentication and access control of IoT devices has become a huge challenge. In the IoT environment, the traditional device identity and access management methods based on public key infrastructure (PKI) and blockchain bring the problems of single point failure and scalability. The emergence of decentralized storage system and distributed ledger technology directed acyclic graph (DAG) provide new ideas to solve these problems. In the paper, we use distributed ledger technology IOTA with low energy demand to design a lightweight and scalable mechanism to manage the identity of IoT devices and access control of large-scale IoT data, so as to ensure source reliability and sharing security of IoT data. In the new scheme, IOTA technology is used to realize the functions of registration, update, revocation and retrieval of the identity of IoT devices, which solves the defects caused by centralized management. Inter planetary file system (IPFS) is used to store a large amount of data generated by devices in the IoT, which not only reduces the burden on devices and systems of IoT, but also solves the single point of failure, delay and other problems caused by traditional cloud storage. At the same time, the fog node is used to provide localized computing for a group of IoT devices to solve the problem of limited computing resources of IoT devices. In addition, in order to prevent data abuse caused by unauthorized devices accessing network resources, a trusted access control mechanism is designed to achieve fine-grained access control, access policy update and other functions, so as to better maintain the IoT environment and network security. Finally, the performance of the proposed scheme is evaluated through simulation experiments, and the security and functions of the scheme are analyzed, which proves that the scheme meets the security demands of the IoT environment.
Bent partitions

Spread and partial spread constructions are the most powerful bent function constructions. A large variety of bent functions from a 2m-dimensional vector space $${\mathbb {V}}_{2m}^{(p)}$$ over $${\mathbb {F}}_p$$ into $${\mathbb {F}}_p$$ can be generated, which are constant on the sets of a partition of $${\mathbb {V}}_{2m}^{(p)}$$ obtained with the subspaces of the (partial) spread. Moreover, from spreads one obtains not only bent functions between elementary abelian groups, but bent functions from $${\mathbb {V}}_{2m}^{(p)}$$ to B, where B can be any abelian group of order $$p^k$$ , $$k\le m$$ . As recently shown (Meidl, Pirsic 2021), partitions from spreads are not the only partitions of $${\mathbb {V}}_{2m}^{(2)}$$ , with these remarkable properties. In this article we present first such partitions—other than (partial) spreads—which we call bent partitions, for $${\mathbb {V}}_{2m}^{(p)}$$ , p odd. We investigate general properties of bent partitions, like number and cardinality of the subsets of the partition. We show that with bent partitions we can construct bent functions from $${\mathbb {V}}_{2m}^{(p)}$$ into a cyclic group $${\mathbb {Z}}_{p^k}$$ . With these results, we obtain the first constructions of bent functions from $${\mathbb {V}}_{2m}^{(p)}$$ into $${\mathbb {Z}}_{p^k}$$ , p odd, which provably do not come from (partial) spreads.
A deep learning model for incorporating temporal information in haze removal

Haze contamination is a very common issue in remote sensing images, which inevitably limits data usability and further applications. Several methods have been developed for haze removal, which is an ill-posed problem. However, most of these methods involve various strong assumptions coupled with manually-determined parameters, which limit their generalization to different scenarios. Moreover, temporal information amongst time-series images has rarely been considered in haze removal. In this paper, the temporal information is proposed to be incorporated for more reliable haze removal, and guided by this general idea, a temporal information injection network (TIIN) is developed. The proposed TIIN solution for haze removal extracts the useful information in the temporally neighboring images provided by the regular revisit of satellite sensors. The TIIN method is suitable for images with various haze levels. Moreover, TIIN is also applicable for temporal neighbors with inherent haze or land cover changes due to a long-time interval between images. The proposed method was validated through experiments on both simulated and real haze images as well as comparison with five state-of-the-art benchmark methods. This research provides a new paradigm for enhancing haze removal by incorporating temporally neighboring images.
Secure IoT in the Era of Quantum Computers—Where Are the Bottlenecks?

Recent progress in quantum computers severely endangers the security of widely used public-key cryptosystems and of all communication that relies on it. Thus, the US NIST is currently exploring new post-quantum cryptographic algorithms that are robust against quantum computers. Security is seen as one of the most critical issues of low-power IoT devices-even with pre-quantum public-key cryptography-since IoT devices have tight energy constraints, limited computational power and strict memory limitations. In this paper, we present, to the best of our knowledge, the first in-depth investigation of the application of potential post-quantum key encapsulation mechanisms (KEMs) and digital signature algorithms (DSAs) proposed in the related US NIST process to a state-of-the-art, TLS-based, low-power IoT infrastructure. We implemented these new KEMs and DSAs in such a representative infrastructure and measured their impact on energy consumption, latency and memory requirements during TLS handshakes on an IoT edge device. Based on our investigations, we gained the following new insights. First, we show that the main contributor to high TLS handshake latency is the higher bandwidth requirement of post-quantum primitives rather than the cryptographic computation itself. Second, we demonstrate that a smart combination of multiple DSAs yields the most energy-, latency- and memory-efficient public key infrastructures, in contrast to NIST's goal to standardize only one algorithm. Third, we show that code-based, isogeny-based and lattice-based algorithms can be implemented on a low-power IoT edge device based on an off-the-shelf Cortex M4 microcontroller while maintaining viable battery runtimes. This is contrary to much research that claims dedicated hardware accelerators are mandatory.
Sentiment Analysis on Online Transportation Reviews Using Word2Vec Text Embedding Model Feature Extraction and Support Vector Machine (SVM) Algorithm

In the era of society 5.0, information technology is growing rapidly, one of which is in the field of transportation. The phenomenon of online transportation services is becoming increasingly popular among the public. With this phenomenon, many people have an opinion about online transportation services, both positive and negative comments. The purpose of this study is to conduct sentiment analysis on online transportation service applications, namely reviews of users of the Gojek and Grab applications on the Google Play Store. This research uses the word2vec text embedding model and the support vector machine (SVM) algorithm. Word2vec is used as a feature extraction model as a representation of words into vector form. The architecture of the word2vec model used is the skip-gram model. The Support Vector Machine (SVM) algorithm is used for the data classification process to determine the level of accuracy of the data sentiment used. The results of the tests carried out on the classification of sentiment analysis on online transportation applications show that the performance results are quite good namely, the Gojek application gets a higher performance value with an accuracy value of 89%, precision of 94%, re-call of 86% and f1-score of 90%. Meanwhile, the Grab application has an accuracy value of 87%, a precision of 94%, a re-call of 85%, and an f1-score of 89%.
Knowledge of university teachers on the use of digital resources to assist people with disabilities. The case of Spain

The integration of Information and Communication Technologies (ICT) in the context of higher education and in the framework of an education in equality and equity requires a competent teaching staff both from a technological and pedagogical point of view. In this context, and with the aim of going deeper into one of these theoretical premises, this study aimed to identify the degree of training and technological knowledge of university teaching staff in the faculties of education in Spain with regard to the use of ICT to support people with disabilities. A cross-sectional research design with a descriptive and predictive approach was used, in which the sample consisted of 2072 university teachers. An ad-hoc questionnaire was used as a data collection instrument. The results revealed the low level of competences of teachers regarding the use of ICT with students with disabilities, where gender and age variables are not relevant to predict the level of digital competence. In this sense, the establishment of specific plans for teacher training and advice on the use of technologies that can help people with disabilities is discussed.
An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the P. falciparum stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments.
YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings

We directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model.We used 1080 images to train the YOLOv4, plus 100 images with proven breast cancer (BC) and 100 images with proven absence of BC to test both models.the rates of true-positive, false-positive and false-negative outcomes were 60, 10 and 40, respectively, for YOLOv4, and 93, 63 and 7, respectively, for NCA. The sensitivities for the YOLOv4 and the NCA were comparable to each other for star-like lesions, masses with unclear borders, round- or oval-shaped masses with clear borders and partly visualized masses. On the contrary, the NCA was superior to the YOLOv4 in the case of asymmetric density and of changes invisible on the dense parenchyma background. Radiologists changed their earlier decisions in six cases per 100 for NCA. YOLOv4 outputs did not influence the radiologists' decisions.in our set, NCA clinically significantly surpasses YOLOv4.
Integrating Healthcare Services Using Blockchain-Based Telehealth Framework

Blockchain technology (BT) has a wide range of built-in features, such as decentralization, transparency, data provenance, security, immutability, and has moved beyond the hype to practical applications in the healthcare industry. Telehealth turns out to be the most efficient and effective way of dispensing healthcare services even in remote areas. Though telehealth has the proven potential to improve the quality of healthcare, its implementation and adoption remain far from ideal. There is a need for a telehealth system that not only ensures the privacy and security of the users but also provides authentic services that enhance the trust to the highest level. Existing applications are leveraging BT and providing limited telehealth services. These applications are focused on a few services; hence do not cover all aspects of healthcare. To unveil the true potential of telehealth, this paper aims to develop an effective telehealth framework-BlockHeal which integrates all essential healthcare services under one platform and ensures a full-fledged trusted environment. The methodology employed includes survey of existing telehealth system, identifying their weaknesses and critical reasons behind their lack of widespread adoption. The proposed framework addresses the limitations and includes all stakeholders of the healthcare system instigating a consolidated platform to ensure authentic, safe, and timely healthcare facilities. Additionally, as the proposed framework is based on BT, it ensures the provision of secure, fault-tolerant, transparent, and tamper-proof data. Moreover, it offers decentralized storage via hyperledger fabric and a collection of decentralized applications (DApps). Finally, the effectiveness of the BlockHeal framework is validated by demonstrating several use-cases.