Reviewerly/exHarmony huggingface dataset:
Deep learning model for regional solar radiation estimation using satellite images

Solar energy is one of the most prominent renewable energy sources today and it can contribute to the energy market with accurate solar radiation estimation. This study proposes a new deep learning-based solar radiation forecasting model using solar radiation data (W/m2) obtained from a ground-based Meteorology measurement station in Hakkari, Turkey, and satellite images taken simultaneously from the European Meteorological Satellites Utilization Organization (EUMETSAT). The model based on convolutional neural networks (CNN) framework adequately extracts the features inherent in multilayer satellite images used as input and obtains more accurate prediction results than traditional models. The prediction performance of the fully-trained CNN model was evaluated using different error metrics such as mean square error (MSE), mean absolute error (MAE), root mean squared error (RMSE), normalized root mean squared error (nRMSE), and coefficient of determination (R2). The results show that the proposed model can accurately predict solar radiation without the need for any measuring device, by making successful meteorological inferences from satellite images with 0.015% MSE, 8.21% nRMSE and 99.8% R2 ratios. Furthermore, the proposed model can be used to verify and calibrate global solar radiation measured from the ground within other locations around the world with similar geographic and climatological characteristics.
Privacy-Aware Architectures for NFC and RFID Sensors in Healthcare Applications

World population and life expectancy have increased steadily in recent years, raising issues regarding access to medical treatments and related expenses. Through last-generation medical sensors, NFC (Near Field Communication) and radio frequency identification (RFID) technologies can enable healthcare internet of things (H-IoT) systems to improve the quality of care while reducing costs. Moreover, the adoption of point-of-care (PoC) testing, performed whenever care is needed to return prompt feedback to the patient, can generate great synergy with NFC/RFID H-IoT systems. However, medical data are extremely sensitive and require careful management and storage to protect patients from malicious actors, so secure system architectures must be conceived for real scenarios. Existing studies do not analyze the security of raw data from the radiofrequency link to cloud-based sharing. Therefore, two novel cloud-based system architectures for data collected from NFC/RFID medical sensors are proposed in this paper. Privacy during data collection is ensured using a set of classical countermeasures selected based on the scientific literature. Then, data can be shared with the medical team using one of two architectures: in the first one, the medical system manages all data accesses, whereas in the second one, the patient defines the access policies. Comprehensive analysis of the H-IoT system can be useful for fostering research on the security of wearable wireless sensors. Moreover, the proposed architectures can be implemented for deploying and testing NFC/RFID-based healthcare applications, such as, for instance, domestic PoCs.
A Novel Heterogeneous Network for Modeling Driver Attention With Multi-Level Visual Content

Driver attention modeling is a crucial technique in building human-centric intelligent driving systems. Considering the human visual mechanism, this study leverages multi-level visual content, including low-level texture features, middle-level optical flows, and high-level semantic information, as the model input. Subsequently, a heterogeneous model is proposed to handle the multi-level input, which integrates the graph and convolutional neural networks. Distinguished from the existing studies that use semantic segmentation, our study directly leverages the objection detection information in an interpretable manner. To deal with the detected objects, in this work, a graph attention network is used to explicitly construct the semantic information, rather than handle the features extracted by convolutional modules for building the latent space features, which are used in existing studies. Further, a semantic attention module is proposed to integrate the non-Euclidean output of the graph network with the Euclidean feature maps of the convolutional neural networks. Finally, these integrated features are decoded to generate a driver attention map. Three typical datasets are used to validate the proposed method. A comprehensive comparison and analysis have proven the feasibility and validity of our proposed method, as well as its ability to achieve state-of-the-art performance.
TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags

So far, discontinuous named entity recognition (NER) has received increasing research attention and many related methods have surged such as hypergraph-based methods, span-based methods, and sequence-to-sequence (Seq2Seq) methods, etc. However, these methods more or less suffer from some problems such as decoding ambiguity and efficiency, which limit their performance. Recently, grid-tagging methods, which benefit from the flexible design of tagging systems and model architectures, have shown superiority to adapt for various information extraction tasks. In this paper, we follow the line of such methods and propose a competitive grid-tagging model for discontinuous NER. We call our model TOE because we incorporate two kinds of Tag-Oriented Enhancement mechanisms into a state-of-the-art (SOTA) grid-tagging model that casts the NER problem into word-word relationship prediction. First, we design a Tag Representation Embedding Module (TREM) to force our model to consider not only word-word relationships but also word-tag and tag-tag relationships. Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships. On the other hand, motivated by the Next-Neighboring-Word (NNW) and Tail-Head-Word (THW) tags in the SOTA model, we add two new symmetric tags, namely Previous-Neighboring-Word (PNW) and Head-Tail-Word (HTW), to model more fine-grained word-word relationships and alleviate error propagation from tag prediction. In the experiments of three benchmark datasets, namely CADEC, ShARe13 and ShARe14, our TOE model pushes the SOTA results by about 0.83%, 0.05% and 0.66% in F1, demonstrating its effectiveness.
Disentangled Representations Learning for Multi-target Cross-domain Recommendation

Data sparsity has been a long-standing issue for accurate and trustworthy recommendation systems (RS). To alleviate the problem, many researchers pay much attention to cross-domain recommendation (CDR), which aims at transferring rich knowledge from related source domains to enhance the recommendation performance of sparse target domain. To reach the knowledge transferring purpose, recent CDR works always focus on designing different pairwise directed or undirected information transferring strategies between source and target domains. However, such pairwise transferring idea is difficult to adapt to multi-target CDR scenarios directly, e.g., transferring knowledge between multiple domains and improving their performance simultaneously, as such strategies may lead the following issues: (1) When the number of domains increases, the number of transferring modules will grow exponentially, which causes heavy computation complexity. (2) A single pairwise transferring module could only capture the relevant information of two domains, but ignores the correlated information of other domains, which may limit the transferring effectiveness. (3) When a sparse domain serves as the source domain during the pairwise transferring, it would easily leads the negative transfer problem, and the untrustworthy information may hurt the target domain recommendation performance. In this article, we consider the key challenge of the multi-target CDR task: How to identify the most valuable trustworthy information over multiple domains and transfer such information efficiently to avoid the negative transfer problem? To fulfill the above challenge, we propose a novel end-to-end model termed as DR-MTCDR , standing for D isentangled R epresentations learning for M ulti- T arget CDR . DR-MTCDR aims at transferring the trustworthy domain-shared information across domains, which has the two major advantages in both efficiency and effectiveness: (1) For efficiency, DR-MTCDR utilizes a unified module on all domains to capture disentangled domain-shared information and domain-specific information, which could support all domain recommendation and be insensitive to the number of domains. (2) For effectiveness, based on the disentangled domain-shared and domain-specific information, DR-MTCDR has the capability to lead positive effect and make trustworthy recommendation for each domain. Empirical evaluations on datasets from both public datasets and real-world large-scale financial datasets have shown that the proposed framework outperforms other state-of-the-art baselines.
Biomedical image segmentation method based on contour preparation

The methods of processing biomedical images, namely thermal images, are investigated. Algorithms for calculating the temperature and area of the zone of interest in the manual mode operator-computer, as well as in the automatic mode, are specified. Methods of thermal image processing are presented, namely recursive generalized contour preparation and preparation based on histograms of connections. An experimental study of these methods was performed, as well as a comparison of thermal image segmentation methods in manual segmentation modes, using contour preparation-based segmentation, multilevel segmentation based on recursive generalized contour preparation, and automatic segmentation based on connectivity histograms.
A reinforcement learning based computational intelligence approach for binary optimization problems: The case of the set-union knapsack problem

As today's one of the hottest topics, machine learning brings about opportunities in various research areas. Moreover, computational intelligence and metaheuristics open up new strategies, which are shown to be efficient in solving optimization problems. However, studies bringing such remarkable approaches together are still lacking. In this context, the present paper introduces a Q-learning reinforcement learning strategy for binary optimization problems. The developed algorithm works as a reinforcement and recommendation system that evaluates the used algorithms, assigns rewards, promotes or demotes them. Thus, it invokes more promising optimizers more frequently. The proposed Q-learning algorithm uses Particle Swarm Optimization (PSO), Genetic Algorithm and a hybrid of these algorithms, namely, genetic-based PSO (gbPSO) as optimizers. Therefore, it is aimed to avoid local optima by using various optimizers and gathering additional statistical data. Secondarily, all optimizers are further enhanced by adopting an initial solution generation technique and triggered random immigrants mechanism to preserve swarm diversity. In addition to these procedures, a mutation procedure that decreases the diversity is adopted. Thus, more intensified search is encouraged towards the end of search. Moreover, while PSO requires for transfer functions in order to perform in binary spaces, the adopted and further improved gbPSO does not necessarily need such auxiliary procedures. Finally, the performances of all used algorithms are analysed on a recently caught on binary problem, namely, the set-union knapsack problem, which has a wide range of real-life applications. As demonstrated by the comprehensive experimental study and appropriate statistical tests, promising improvements are achieved.
Self-diagnosis platform via IOT-based privacy preserving medical data

Healthcare is one of the application domains that use IoT technology, where sensors and IoT-enabled medical devices transmit data to healthcare specialists without the need for human intervention. The health records of previous patients are both very confidential and crucial to the identification of the disease for current patients. Therefore, it is important to fully utilize the records of previous patients without disclosing their personal data. This paper proposes an innovative Self Diagnosis Platform (SDP) that securely retrieves patients' records from past records without disclosing either the privacy of the patient or the record database. Initially, the patient device receives data from the sensors placed in the patient's body, which is then encrypted by using Edwards' Digital Signature Algorithm. The encrypted data is then sent to the Secure Disease Archive (SDA), which contains past records. Based on the patient's data, similar records will be retrieved from the database using the SDP. The suggested framework is compared with traditional methods in terms of encryption time, execution time, and end-to-end delay. Experimental results indicate that the suggested SDP technique achieves a better execution time of 30.63%, 27.48%, 22.07%, and 9.23% than LDQN, SE-AC, PMDA, and EPPDA methods. This system is more effectiveand secure for real-time applications.
Ag<sub>2</sub>S island network reservoir that works with direct optical signal inputs

Abstract A physical reservoir that accepts direct light irradiation as input was developed using a Ag 2 S island network. Short-term memory and nonlinearity required for reservoirs are achieved by the diffusion of Ag + cations in each Ag 2 S island and the growth of Ag filaments between Ag 2 S islands. We found that direct light irradiation to Ag 2 S islands changes local conductivity in a reservoir, which enhances the performance in short-term memory and nonlinearity of the reservoir. Using the effect, we performed a pattern classification of light that was irradiated to a Ag 2 S island network reservoir through a rectangular slit, which resulted in the accuracy of over 95%.
Secure user authentication and key agreement scheme for IoT device access control based smart home communications

The upcoming paradigm in Internet of Things (IoT) based applications is to afford effective interactional communication strategies between the devices in the smart home system. With the rapid growth of IoT services, the incorporation of security measures becomes a vital concern. The general issue faced in the security of the intercommunication between the devices and the users is improper authentication between them. Also, the access control of devices must be ensured with reliable features for establishing secure communication between the users and devices. Hence, we propose a protocol called Modified Honey Encryption using Inverse Sampling-Conditional Probability Model Transform (MHE-IS-CPMT) with Elliptic Curve Cryptography (ECC) to authenticate and perform the key agreement. Here, we employ the following steps: (1) Initialization, (2) Registration, (3) Login and data access Request, (4) Authentication and Session key agreement, and (5) Key update. At the commencement of the session, the users (u), Mobile Users (MU), and the other devices participating in the smart home system are initialized to the Home network head (H). Then, for the registration process, the user and the devices register them into H via the smart gateway (SG) by providing their own identities. The user details and the data about the devices are secured using the MHE-IS-CPMT with the ECC method. Next, during the login process, the registered users connect to the smart home system and send a request to SG to gain access to the devices. After verification, the user is authenticated and the system enables them to acquire the device access control by providing them with the private key of the device. In addition, the proposed system facilitates the secure key change procedure for the legitimate user to update their key whenever required. Hence, the performance of the model is secured against different types of attacks and also obtains more security features than existing methods.
Face image de‐identification by feature space adversarial perturbation

Summary Privacy leakage in images attracts increasing concerns these days, as photos uploaded to large social platforms are usually not processed by proper privacy protection mechanisms. Moreover, with advanced artificial intelligence (AI) tools such as deep neural network (DNN), an adversary can detect people's identities and collect other sensitive personal information from images at an unprecedented scale. In this paper, we introduce a novel face image de‐identification framework using adversarial perturbations in the feature space. Manipulating the feature space vector ensures the good transferability of our framework. Moreover, the proposed feature space adversarial perturbation generation algorithm can successfully protect the identity‐related information while ensuring the other attributes remain similar. Finally, we conduct extensive experiments on two face image datasets to evaluate the performance of the proposed method. Our results show that the proposed method can generate real‐looking privacy‐preserving images efficiently. Although our framework has only been tested on two real‐life face image datasets, it can be easily extended to other types of images.
A deep journey into image enhancement: A survey of current and emerging trends

Image captured under poor-illumination conditions often display attributes of having poor contrasts, low brightness, a narrow gray range, colour distortions and considerable interference, which seriously affect the qualitative visual effects on human eyes and severely restrict the efficiency of several machine vision systems. In addition, underwater images often suffer from colour shift and contrast degradation because of an absorption and scattering of light while travelling in water. These unpleasant effects limits visibility, reduce contrast and even generate colour casts that limits the use of underwater images and videos in marine archaeology and biology. In medical imaging applications, medical images are important tools for detecting and diagnosing several medical conditions and ailments. However, the quality of medical images can often be degraded during image acquisition due to factors such as noise interference, artefacts, and poor illumination. This may lead to the misdiagnosis of medical conditions, which can further aggravate life threatening situations. Image enhancement is one of the most important technologies in the field of image processing, and its purpose is to improve the quality of images for specific applications. In general, the basic principle of image enhancement is to improve the quality and visual interpretability of an image so that it is more suitable for the specific applications and the observers. Over the last few decades, numerous image enhancement techniques have been proposed in the literature This study covers a systematic survey on existing state-of-the-art image enhancement techniques into broad classification of their algorithms. In addition, this paper summarises the datasets utilised in the literature for performing the experiments. Furthermore, an attention has been drawn towards several evaluation parameters for quantitative evaluation and compared different state-of-the-art algorithms for performance analysis on benchmark datasets. In addition, we discussed the recent areas of applications in image enhancement in detail. Lastly, we have also discussed numerous unresolved open problems and suggested possible future research directions. We believe that by putting forth all our efforts this study may presents a comprehensive resource for the future research.
Building for tomorrow: Assessing the temporal persistence of text classifiers

Performance of text classification models tends to drop over time due to changes in data, which limits the lifetime of a pretrained model. Therefore an ability to predict a model's ability to persist over time can help design models that can be effectively used over a longer period of time. In this paper, we provide a thorough discussion into the problem, establish an evaluation setup for the task. We look at this problem from a practical perspective by assessing the ability of a wide range of language models and classification algorithms to persist over time, as well as how dataset characteristics can help predict the temporal stability of different models. We perform longitudinal classification experiments on three datasets spanning between 6 and 19 years, and involving diverse tasks and types of data. By splitting the longitudinal datasets into years, we perform a comprehensive set of experiments by training and testing across data that are different numbers of years apart from each other, both in the past and in the future. This enables a gradual investigation into the impact of the temporal gap between training and test sets on the classification performance, as well as measuring the extent of the persistence over time. Through experimenting with a range of language models and algorithms, we observe a consistent trend of performance drop over time, which however differs significantly across datasets; indeed, datasets whose domain is more closed and language is more stable, such as with book reviews, exhibit a less pronounced performance drop than open-domain social media datasets where language varies significantly more. We find that one can estimate how a model will retain its performance over time based on (i) how well the model performs over a restricted time period and its extrapolation to a longer time period, and (ii) the linguistic characteristics of the dataset, such as the familiarity score between subsets from different years. Findings from these experiments have important implications for the design of text classification models with the aim of preserving performance over time.
An introduction to eye tracking in human factors healthcare research and medical device testing

Eye tracking is a powerful and sophisticated tool that provides an objective glimpse into the cognition of healthcare providers, patients, caregivers, and medical device users. Insights gleaned from eye tracking can be harnessed to better understand – and ultimately improve – the dynamics of healthcare, which quite literally has the potential to save lives. Nonetheless, the use of eye tracking within healthcare research and medical device testing remains in its infancy, which at least partly reflects the learning curve that it demands. As such, the central aim of this article is to provide an easily digestible primer for healthcare researchers and practitioners interested in first getting started with eye tracking. The discussion offers a general overview of how it works, device types and notable specifications, a taxonomy of common metrics, and various sensible best practices and recommendations tailored to the use of wearable eye trackers in a high-fidelity simulated use study context.
A real time secured medical management system based on blockchain and internet of things

Nowadays Electronic Health Care System is growing to a large extent. As data retrieve from IoT devices, that data is needed to store in a database as the different patients have different data. Due to a lack of intrinsic security safeguards, the Internet of Things is prone to privacy and security breaches. The information gathered from IoT devices is mostly saved in a database This data is very important for a particular patient. If this data is altered by any intruder, then the doctor cannot find the actual problem of the patient and also cannot give the patient proper treatment, as a result, it causes great harm to the patient. As a result, a security mechanism for the data contained in the database is required. Blockchain technology will help a lot in this situation. We make an IoT-based prototype that uses Blockchain technology to get rid of this anonymous data access, in the common word patient's data are private through this system.