Reviewerly/exHarmony huggingface dataset:
Detecting Soldiers’ Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities

Objectively determining soldiers' fatigue levels could help prevent injuries or accidents resulting from inattention or decreased alertness. Eye-tracking technologies, such as optical eye tracking (OET) and electrooculography (EOG), are often used to monitor fatigue. Eyeblinks-especially blink frequency and blink duration-are known as easily observable and valid biomarkers of fatigue. Currently, various eye trackers (i.e., eye-tracking glasses) are available on the market using either OET or EOG technologies. These wearable eye trackers offer several advantages, including unobtrusive functionality, practicality, and low costs. However, several challenges and limitations must be considered when implementing these technologies in the field to monitor fatigue levels. This review investigates the feasibility of eye tracking in the field focusing on the practical applications in military operational environments.This paper summarizes the existing literature about eyeblink dynamics and available wearable eye-tracking technologies, exposing challenges and limitations, as well as discussing practical recommendations on how to improve the feasibility of eye tracking in the field.So far, no eye-tracking glasses can be recommended for use in a demanding work environment. First, eyeblink dynamics are influenced by multiple factors; therefore, environments, situations, and individual behavior must be taken into account. Second, the glasses' placement, sunlight, facial or body movements, vibrations, and sweat can drastically decrease measurement accuracy. The placement of the eye cameras for the OET and the placement of the electrodes for the EOG must be chosen consciously, the sampling rate must be minimal 200 Hz, and software and hardware must be robust to resist any factors influencing eye tracking.Monitoring physiological and psychological readiness of soldiers, as well as other civil professionals that face higher risks when their attention is impaired or reduced, is necessary. However, improvements to eye-tracking devices' hardware, calibration method, sampling rate, and algorithm are needed in order to accurately monitor fatigue levels in the field.
Novel Hybrid Intelligent Secure Cloud Internet of Things Based Disease Prediction and Diagnosis

Nowadays, more people are affected by various diseases such as blood pressure, heart failure, etc. The early prediction of diseases tends to increase the survival of affected patients by allowing preventive action. A key element for this purpose is the digitalization of the healthcare system through the Internet of Things (IoT) and cloud computing. Nevertheless, there are major problems in the cloud with the IoT due to false predictions and errors in medical data, which results in taking a longer time to receive patient details and not providing the best outcome. Data transfer through the cloud can also be hacked by attackers due to the lack of security. This leads to a challenge for medical experts to predict the diseases accurately for a specific patient. Therefore, a novel hybrid elapid encryption (HEE) method was proposed for improving the security of cloud systems. In addition, the affected person’s disease and the severity risk level were predicted and classified using the proposed novel hybridization technique of the generalized-fuzzy-intelligence-based gray wolf ant lion optimization (GFI-GWALO) method. After the disease is predicted, the alert signal is provided to the patients. Moreover, this proposed research was implemented on MATLAB. Then the proposed simulation outcome was compared with various conventional methods and showed that the proposed method has the best outcomes in terms of its security and disease prediction with 80 ms of encryption time and 78 ms of decryption time, 100% accuracy, 99.50% precision and 8 ms of processing time.
StaR: Breaking the Scalability Limit for RDMA

Due to its superior performance, Remote Direct Memory Access (RDMA) has been widely deployed in data center networks. It provides applications with ultra-high throughput, ultra-low latency, and far lower CPU utilization than TCP/IP software network stack. However, the connection states that must be stored on the RDMA NIC (RNIC) and the small NIC memory result in poor scalability. The performance drops significantly when the RNIC needs to maintain a large number of concurrent connections.We propose StaR (Stateless RDMA), which solves the scalability problem of RDMA by transferring states to the other communication end. Leveraging the asymmetric communication pattern in data center applications, StaR lets the communication end with low concurrency save states for the other end with high concurrency, thus making the RNIC on the bottleneck side to be stateless. We have implemented StaR on an FPGA board with 10Gbps network port and evaluated its performance on a testbed with 9 machines all equipped with StaR NICs. The experimental results show that in high concurrency scenarios, the throughput of StaR can reach up to 4.13x and 1.35x of the original RNIC and the latest software-based solution, respectively.
A novel framework for detection of motion and appearance-based Anomaly using ensemble learning and LSTMs

The variable time-dependent densities in crowd motion and several occlusions in real scenarios make the task of spotting anomalies very laborious. Also, an anomalous entity can be perceived to be non-anomalous from a different angle of perception. The proposed methodology introduces a novel framework for crowd anomaly detection at a patch level by integrating a thread of bi-directional LSTM for motion-based Anomaly and a thread of ensemble learning which uses pre-trained CONV-nets to learn appearance-based anomalies. A novel post-processing step to address the underlying false predictions using a threshold comparator system is also introduced. The proposed methodology is tested on the benchmark datasets, namely UCSD PED-1, PED-2, and UMN dataset, and it performs comparably with the other existing methods in the literature.
Digital trust substitution technologies to support smallholder livelihoods in Sub-Saharan Africa

Blockchain technology has the potential to reduce the need for trust and to ensure accountability and equity in smallholder agricultural development. We present two applications – one already in progress and one hypothetical - using blockchain to address market failures in the smallholder sector in Sub-Saharan Africa. First, we explore BanQu, a blockchain-based platform used by Anheuser-Busch InBev to create verified financial identities for smallholders along the supply chain. Second, we discuss a hypothetical blockchain application to improve the reliability of actors within smallholder dairy cooperatives. Blockchain applications in smallholder agriculture could be transformative, serving as substitutes for trust, but alone blockchain is not a solution to the multiple challenges facing the smallholder farming sector.
Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI’s detection, training deep learning, and machine learning networks to classify AD’s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.
Few-shot learning based on hierarchical classification via multi-granularity relation networks

Few-shot learning is one of the significant areas of machine learning, which aims to recognize novel visual classes from few labeled examples. Many existing models make full use of the similarity of inner-class features and achieve satisfactory results. However, these models assume that classes are independent of each other, ignoring the inter-class relationship. In this paper, we propose a few-shot hierarchical classification model via multi-granularity relation networks (HMRN) considering both the inner-class similarity and inter-class relationship. The multi-granularity relationship among coarse- and fine-grained classes is an important auxiliary information in the class hierarchical structure originated from data. Thus, we first extract hierarchical features of different granularity classes according to the membership relationship among the classes. Second, we build multi-granularity relation networks to obtain the inner-class similarity relation of different granularity classes using the hierarchical features. Finally, we consider the tradeoff among the inner-class similarity relation of different granularity classes for hierarchical few-shot learning, which takes the information of coarse-grained classes to assist the learning of fine-grained classes. Experimental results show that our model outperforms several state-of-the-art flat (without hierarchical structure) models and hierarchical models. For example, the accuracy of HMRN is about 3.00% better than that of flat models on the tieredImageNet dataset.
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications

AI healthcare applications rely on sensitive electronic healthcare records (EHRs) that are scarcely labelled and are often distributed across a network of the symbiont institutions. It is challenging to train the effective machine learning models on such data. In this work, we propose dynamic neural graphs based federated learning framework to address these challenges. The proposed framework extends Reptile, a model agnostic meta-learning (MAML) algorithm, to a federated setting. However, unlike the existing MAML algorithms, this paper proposes a dynamic variant of neural graph learning (NGL) to incorporate unlabelled examples in the supervised training setup. Dynamic NGL computes a meta-learning update by performing supervised learning on a labelled training example while performing metric learning on its labelled or unlabelled neighbourhood. This neighbourhood of a labelled example is established dynamically using local graphs built over the batches of training examples. Each local graph is constructed by comparing the similarity between embedding generated by the current state of the model. The introduction of metric learning on the neighbourhood makes this framework semi-supervised in nature. The experimental results on the publicly available MIMIC-III dataset highlight the effectiveness of the proposed framework for both single and multi-task settings under data decentralisation constraints and limited supervision.
The Initial Coin Offering (ICO) Process: Regulation and Risks

ICOs are very attractive for investors and issuers. ICOs allow funding raising in exchange for cryptographically secure tokens, which are a means of paying for future projects or services. However, there is insignificant regulation of this process all over the world. Some countries have banned crypto assets; others have allowed the free use of tokens but do not give them official status. In this paper, the authors present an overview of the legal regulation of ICOs in different countries, dividing them into three groups: in the first group are the countries with developed legal norms and rules for conducting ICO, they have the subsequent circulation of tokens on their territory; in the second group are the countries that are most friendly to ICOs; the third group of countries has a wait-and-see attitude. The author connect the insufficient law regulation and risks of ICOs in different countries. The types of ICO risks are divided into three main categories: financial, technical, and analytical. The main ways to reduce these risks, depending on their types, are highlighted in this study. They are connected with the improvement of the legal regulation of the publication of a White Paper, the KYC procedure, and the involvement of escrow agents.
Management of Access Control for Decentralized Online Educations using Blockchain Technology

The rapid development of information technology opens up opportunities for the development of better services in the field of education. With internet services, the information that students have is not limited by space, place, time and distance. Created a network that does not have a service provider acting as a central authority and users have access to control information that avoids the presence of third parties. An alternative solution to today’s centralized networked learning systems uses the efficient and distributed Decentralized Online Educations (DOE). This platform uses blockchain technology as an educational data storage system that is used to solve academic privacy issues in a decentralized system. This study, created a manageable and auditable access control framework for Decentralized Online Education (DOE) using blockchain technology to discuss the definition of privacy policy.
Effect of the random forests with recursive feature elimination for breast cancer classification

A breast cancer is the most dangerous disease of the death cause among aged 40–55 women. We need a computer aided diagnosis system for breast cancer classification. In the previous study, the random forests was reported to be one of promising calssifiers for classifying breast cancers. This paper presents the effect of the random forests with recursive feature elimination for breast cancer classification, compered to the state of the art classification techniques, such as XGBosst and LGBM.
Under the hood of the Ethereum blockchain

Since the creation of Bitcoin, there has been an explosion in the number of cryptocurrencies developed and although Ethereum is the second largest cryptocurrency in terms of market capitalization, there is very little research on this cryptocurrency and in this paper, we provide an overview of this cryptocurrency. We examine the addresses, transactions and fees as well as users holding patterns to the Ethereum blockchain. Therefore this paper offers the first detailed overview of the Ethereum blockchain.
Att-BiL-SL: Attention-Based Bi-LSTM and Sequential LSTM for Describing Video in the Textual Formation

With the advancement of the technological field, day by day, people from around the world are having easier access to internet abled devices, and as a result, video data is growing rapidly. The increase of portable devices such as various action cameras, mobile cameras, motion cameras, etc., can also be considered for the faster growth of video data. Data from these multiple sources need more maintenance to process for various usages according to the needs. By considering these enormous amounts of video data, it cannot be navigated fully by the end-users. Throughout recent times, many research works have been done to generate descriptions from the images or visual scene recordings to address the mentioned issue. This description generation, also known as video captioning, is more complex than single image captioning. Various advanced neural networks have been used in various studies to perform video captioning. In this paper, we propose an attention-based Bi-LSTM and sequential LSTM (Att-BiL-SL) encoder-decoder model for describing the video in textual format. The model consists of two-layer attention-based bi-LSTM and one-layer sequential LSTM for video captioning. The model also extracts the universal and native temporal features from the video frames for smooth sentence generation from optical frames. This paper includes the word embedding with a soft attention mechanism and a beam search optimization algorithm to generate qualitative results. It is found that the architecture proposed in this paper performs better than various existing state of the art models.
FELT-ABKS: Fog-Enabled Lightweight Traceable Attribute-Based Keyword Search Over Encrypted Data

Attribute-based keyword search (ABKS) achieves privacy-preserving keyword search and fine-grained access control over encrypted data in the cloud. However, existing ABKS schemes cannot be directly applied for resource-constrained (such as Internet of Things) devices due to heavy computation overhead. In addition, identifying the malicious user who misuses the secret key is difficult if more than one user is having the same set of attributes. Furthermore, user revocation and attribute revocation are two important challenges in real-world applications. To address these challenges, this article proposes a FELT-ABKS: fog-enabled lightweight traceable ABKS over encrypted data by using ciphertext-policy ABKS to realize keyword search and fine-grained access control. FELT-ABKS achieves minimal computation cost at end users by transferring maximum computation to fog nodes. Furthermore, FELT-ABKS traces the malicious users who misuse their secret key. Besides, it supports user revocation and attribute revocation. The security analysis proves that FELT-ABKS is secure against the chosen keyword attack, chosen-plaintext attack, and modify secret key attack. Finally, experiments demonstrate that FELT-ABKS is lightweight and feasible.
Low Dose CT Image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss

Image denoising of Low-dose computed tomography (LDCT) images has continues to receive attention in the research community due to ongoing concerns about high-dose radiation exposure of patients for diagnosis. The use of low radiation CT image, however, could lead to inaccurate diagnosis due to the presence of noise. Deep learning techniques are being integrated into denoising methods to address this problem. In this paper, a General Adversarial Network (GAN) composed of boosting fusion of spatial and channel attention modules is proposed. These modules are embedded in the denoiser to address the limitations of other GAN-based denoising models that tend to only focus on the local processing and neglect the dependencies of creating feature maps with spatial- and channel- wise image characteristics. This study aims to preserve structural details of LDCT images by applying boosting attention modules, prevents edge over-smoothing by integrating perceptual loss via VGG16 pre-trained network, and finally, improves the computational efficiency by taking advantage of deep learning techniques and GPU parallel computation.
BAFL: An Efficient Blockchain-Based Asynchronous Federated Learning Framework

With the widespread of 5G networks, the application of Federated Learning (FL) in Internet of Things (IoT) has become a trend. However, the trust problem caused by the centralized aggregation server, and the inefficiency problem caused by the low-performance devices, are still key challenges. Several studies involving asynchronous FL have been conducted to accelerate the training process, but they usually have a decreased model performance. In this paper, a blockchain-based asynchronous federated learning framework with a dynamic scaling factor is proposed. By adopting the blockchain, the trust problem among devices can be addressed. Meanwhile, the novel dynamic scaling factor is proposed to help improve the FL efficiency and accuracy. Extensive experiments are conducted on heterogeneous devices and the results show that the proposed framework mitigates the impact of low-performance devices while being as efficient as traditional FL with the extra benefit of alleviating the trust problem among IoT devices.
Human action recognition based on spatial–temporal relational model and LSTM-CNN framework

Due to the increasing popularity of human skeleton capture systems, many new methods for implementing skeleton-based action recognition has been proposed. Some of these include Long Term Memory and Convolutional Neural Networks. These methods can investigate the significant spatial-temporal information, but they are limited in their capacity to do so in real-world scenarios. In this paper, a new spatial-temporal model with a bi-temporal end-to-end framework is proposed. A novel structure is proposed to combine the functions LSTM and CNN. The structure uses the dependency model to build the skeleton data for the proposed network.
An information retrieval benchmarking model of satisficing and impatient users’ behavior in online search environments

This study analyzes the effects that the position of the alternatives ranked by a search engine and the relative impatience of users have on their information retrieval behavior. We design a stochastic information retrieval algorithm calibrated to mimic the click-through rates (CTRs) of users observed in real-life environments. We introduce two versions of the mimicking algorithm designed to demonstrate the importance of impatience as a determinant of CTRs conditioned by the alternatives' ranking position. The first version assumes that users proceed sequentially through the ranking until they find an alternative satisficing their expectations. Once they find a satisficing alternative, they continue retrieving information until they observe an alternative that violates their expectations. The second version increases users' impatience, who stop retrieving information as soon as an alternative does not satisfy their expectations – even if it is the top-ranked one. All three algorithmic structures are sufficiently malleable to incorporate any potential modification to users' beliefs and preferences. We simulate sets of 1,000,000 queries to illustrate how the CTRs of the top three ranked alternatives remain stable as users grow impatient, with differences widening as growingly impatient users proceed halfway through the ranking.
A New Event-Triggered Type-3 Fuzzy Control System for Multi-Agent Systems: Optimal Economic Efficient Approach for Actuator Activating

This study presents a new approach for multi-agent systems (MASs). The agent dynamics are approximated by the suggested type-3 (T3) fuzzy logic system (FLS). Some sufficient conditions based on the event-triggered scheme are presented to ensure the stability under less activation of the actuators. New tuning rules are obtained for T3-FLSs form the stability and robustness examination. The effect of perturbations, actuator failures and approximation errors are compensated by the designed adaptive compensators. Simulation results show that the output of all agents well converged to the leader agent under disturbances and faulty conditions. Additionally, it is shown that the suggested event-triggered scheme is effective and the actuators are updated about 20–40% of total sample times.