Reviewerly/exHarmony huggingface dataset:
Implementation of Block-Level Double Encryption Based on Machine Learning Techniques for Attack Detection and Prevention

Cloud computing is one of the most important business models of modern information technology. It provides a minimum of various services to the user interaction and low cost (hardware and software). Cloud services are based on the newline architectures on virtualization by using the multitenancy for better resource management and newline strong isolation between several virtual machines (VMs). The spying on a victim VM is challenging, particularly when one wants to use per-core microarchitectural features as a side channel. For example, the cache contains the most potential for damaging side channels, but shared information across different cores affects the cloud information. To overcome this problem, propose the Secure Block-Level Double Encryption (SBLDE) algorithm for user signature verification in the cloud server. It uses identity-based detection techniques to monitor the colocated VMs to identify abnormal cache data and channel behaviors typically during VM data transformation. The identity-based linear classification (IBLC) method is used for classifying the attacker channel when the data is transferred/retrieved from the VM cloud server. This cloud controller finds the channel misbehavior to block the port or channel, changing other available ports’ communication. The service verification provides strong user access permission on the cloud server when the unknown request to the cloud server suddenly executes the key authentication to verify the user permission. This linear classification trains the existing side-channel attack datasets to the classifier and identifies the VM cloud’s attack channel. The study focused on preventing attacks from interrupting the system and serves as an effective means for cross-VM side-channel attacks. This proposed method protects the cloud data and prevents cross-VM channel attack detection efficiently, compared to other existing methods. In this overall proposed method, SBLDE’s performance is to be evaluated and then compared with the existing method.
USAWEB: An Usability Metrics to Measure the Online Store Consumer's Purchase Experience

The study proposes to create and evaluate the validity and reliability of a scale that allows us to measure the usability of web pages for electronic commerce called USAWEB (web usability), the referred scale is composed of 10 items which makes it an easy-to-apply instrument. The pilot test consisted of 422 participants who reported making purchases online. The items were formulated after having carried out an exhaustive review of the literature to later be evaluated by experts who guaranteed the content of the construct. Exploratory and confirmatory factor analyzes were applied to evaluate the proposed model, the results revealed a one-dimensional scale with a good fit of the model that met all the CMIN, DF, CMIN/DF, CFI, SRMR, RMSEA and PClose indicators satisfactorily. In this way, this scale allows the authors to provide the scientific and business community with a reliable instrument to be applied both in future research that seeks to measure the level of usability of web pages intended for electronic commerce.
Fuzzy prototype selection-based classifiers for imbalanced data. Case study

Imbalanced data are popular in the machine learning community due to their likelihood of appearing in real-world application areas and the problems they present for classical classifiers. The goal of this work is to extend the capabilities of prototype-based classifiers using fuzzy similarity relations and to make them sensitive to class-imbalanced data classification. This paper proposes two new fuzzy logic-based prototype selection classifiers for imbalanced datasets, Imb-SPBASIR-Fuzzy_V1 (FPS-v1) and Imb-SPBASIR-Fuzzy_V2 (FPS-v1), and shows a comparative study of them with state-of-the-art methods on public datasets from the UCI machine learning repository. The results on the selected datasets suggest that fuzzy logic-based prototype selection classifiers perform well and efficiently, indicating that it is a viable alternative. The fuzzy relationships provided by this approach allow better results than the state-of-the-art models. Further analysis showed that the proposed fuzzy-based prototypes methods permit obtaining more accurate to deal with the correct prophylaxis, timely diagnosis and treatment of postoperative mediastinitis.
An Effective Algorithm to Analyze the Optokinetic Nystagmus Waveforms from a Low-Cost Eye Tracker

Most neurological diseases are usually accompanied by changes in the oculomotor nerve. Analysis of different types of eye movements will help provide important information in ophthalmology, neurology, and psychology. At present, many scholars use optokinetic nystagmus (OKN) to study the physiological phenomenon of eye movement. OKN is an involuntary eye movement induced by a large moving surrounding visual field. It consists of a slow pursuing eye movement, called "slow phase" (SP), and a fast re-fixating saccade eye movement, called "fast phase" (FP). Non-invasive video-oculography has been used increasingly in eye movement research. However, research-grade eye trackers are often expensive and less accessible to most researchers. Using a low-cost eye tracker to quantitatively measure OKN eye movement will facilitate the general application of eye movement research.We design an analytical algorithm to quantitatively measure OKN eye movements on a low-cost eye tracker. Using simple conditional filtering, accurate FP positions can be obtained quickly. The high-precision FP recognition rate is of great help for the subsequent calculation of eye movement analysis parameters, such as mean slow phase velocity (MSPV), which is beneficial as a reference index for patients with strabismus and other eye diseases.Experimental results indicate that the proposed method achieves faster and better results than other approaches, and can provide an effective algorithm to calculate and analyze the FP position of OKN waveforms.
Development and Evaluation of a Low-cost Wheelchair Simulator for the Haptic Rendering of Virtual Road Conditions

Many streets and buildings are not accessible for wheelchair users, which impedes a major challenge for their mobility. Often, such challenges can be considered during the planning stage, which is why it is important to include this user group in the planning process. Ideally, these planning efforts should also be made visually and haptically navigable using Virtual Reality (VR) technology to allow for better imagination, accurate conclusions, and awareness of wheelchair users. This work optimizes previous approaches so that different roadway conditions can be made haptically perceptible. The proposed prototype provides a non-contact and adjustable brake for simulating inclined planes, which can be adjusted depending on personal and environmental parameters. In addition, it can simulate roads such as cobblestones using a tactile transducer. The individually optimized components were combined into a complete VR system and integrated into a virtual environment for evaluation. The qualitative and quantitative results showed that realistic simulation is possible, but further development steps towards holistic and dissemination-capable hardware and software are needed. To this end, our contribution aims to improve the long-term involvement of wheelchair users in planning processes and increase awareness of their mobility situation.
A GPU-accelerated light-field super-resolution framework based on mixed noise model and weighted regularization

Abstract Light-field (LF) super-resolution (SR) plays an essential role in alleviating the current technology challenge in the acquisition of a 4D LF, which assembles both high-density angular and spatial information. Due to the algorithm complexity and data-intensive property of LF images, LFSR demands a significant computational effort and results in a long CPU processing time. This paper presents a GPU-accelerated computational framework for reconstructing high-resolution (HR) LF images under a mixed Gaussian-Impulse noise condition. The main focus is on developing a high-performance approach considering processing speed and reconstruction quality. From a statistical perspective, we derive a joint $$\ell ^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> - $$\ell ^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> data fidelity term for penalizing the HR reconstruction error taking into account the mixed noise situation. For regularization, we employ the weighted non-local total variation approach, which allows us to effectively realize LF image prior through a proper weighting scheme. We show that the alternating direction method of the multipliers algorithm (ADMM) can be used to simplify the computation complexity and results in a high-performance parallel computation on the GPU Platform. An extensive experiment is conducted on both synthetic 4D LF dataset and natural image dataset to validate the proposed SR model’s robustness and evaluate the accelerated optimizer’s performance. The experimental results show that our approach achieves better reconstruction quality under severe mixed-noise conditions as compared to the state-of-the-art approaches. In addition, the proposed approach overcomes the limitation of the previous work in handling large-scale SR tasks. While fitting within a single off-the-shelf GPU, the proposed accelerator provides an average speedup of 2.46 $${\times }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> and 1.57 $${\times }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> for $${\times }2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>×</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> and $${\times }3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>×</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> SR tasks, respectively. In addition, a speedup of $$77{\times }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>77</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> is achieved as compared to CPU execution.
Conveying Procedural and Descriptive Knowledge with Augmented Reality

Repairing or maintaining technical systems requires procedural knowledge. When dealing with complex problems, solid descriptive knowledge is also necessary to understand unexpected system states. Consequently, service technicians are taught both descriptive knowledge on product technology as well as procedural knowledge; the first in a theoretical way, the second typically in practical trainings. In the past, augmented reality (AR) training has been proposed for both aspects individually. Here we investigate initial AR training for joint theoretical and practical training. In an experiment under field conditions, this paper compares an initial AR training to conventional personal training for previously unknown repair and maintenance tasks. Our results show that the initial AR training resulted in similar outcomes for short-term and long-term memory of descriptive knowledge compared to the conventional training. Looking at the practical training part, AR outperforms conventional training regarding completion time, repair, and process quality. The results show that AR can provide an alternative to conventional training for unknown repair and maintenance tasks and thus can save financial resources, such as personnel costs (trainer) and costs for physical systems.
An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling

At present, the existing single-pixel position-scrambling technique is not sensitive to the chaotic sequence used, and adjacent-pixel position scrambling has difficulty ensuring a good scrambling effect and speed at the same time. In this paper, a stage-merging scrambling algorithm is proposed, which combines the two-stage scrambling process and can complete the dual scrambling of pixel position and pixel value at the same time. It not only improves the scrambling speed, but also greatly improves the scrambling effects. Then, a complete image encryption and decryption scheme was designed based on stage-merging bit scrambling combined with DNA coding. Security analysis shows that the algorithm can resist various means of attack such as exhaustive attack and differential attack. The research in this paper extends the existing bit-scrambling algorithms and is suitable for practical applications.
A Deep Learning Based Human Fall Detection Solution

Automatic human fall detection is a challenging task in healthcare. Using deep learning models in conjunction with cameras have been proven to be more accurate and efficient in detecting falls in comparison to wearable technology and other more intrusive alternatives. In this paper, we propose a long-term recurrent convolutional network (LRCN) architecture for human fall detection, which consists of a custom convolutional neural network specifically designed to address the challenges of this task and the limited amount of available data, followed by a long short-term memory (LSTM) neural network which decodes sequential information to determine if there has been a fall. Performance evaluations show that our LRCN architecture outperforms the existing state-of-the-art method by 11.20%.
Control Implemented on Quantum Computers: Effects of Noise, Nondeterminism, and Entanglement

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of nondeterminism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives.
Perspectives on Social Health Robots: How Experts’ Views Improved from 2017 to 2021

This work documents the rising acceptance of social robots for healthcare as well as their growing economic potential from 2017 to 2021. The comparison is based on two studies in the active assisted living (AAL) community. We first provide a brief overview of social robotics and a discussion of the economic potential of social health robots. We found that, despite the huge potential for robotic support in healthcare and domestic routines, social robots still lack the functionality to access that potential. At the same time, the study exemplifies a rise in acceptance: all health-related activities are more accepted in 2021 when in 2017, most of them with high statistical significance. When investigating the economic perspective, we found that persons are aware of the influence of cultural, spiritual, or religious beliefs. Most experts (57%), having a European background, expect the state or the government to be the key driver for establishing social robots in health and significantly prefer leasing or renting a social health robot to buying one. Nevertheless, we speculate that it might be a global financial elite which is first to adopt social robots.
Matching attacks on Romulus‐M

This paper considers a problem of identifying matching attacks against Romulus-M, one of the 10 finalists of National Institute of Standards and Technology Lightweight Cryptography standardisation project. Romulus-M is provably secure, that is, there is a theorem statement showing the upper bound on the success probability of attacking the scheme as a function of adversaries' resources. If there exists an attack that matches the provable security bound, then this implies that the attack is optimal and that the bound is tight in the sense that it cannot be improved. It is shown that the security bounds of Romulus-M are tight for a large class of parameters by presenting concrete matching attacks.
In government microblogs we trust: Doing trust work in Chinese government microblogs during COVID-19

This paper presents a novel model for examining trust work in the Chinese government microblogs, integrating Fuoli and Paradis’s model of trust-repair discourse with scholarly views on Chinese trust. We argue that the ultimate communicative goal of government microblogs is to construct the Chinese authorities’ CBT (cognition-based trust) and ABT (affect-based trust). The antecedents of CBT include the trustee’s ability and integrity, and those of ABT include values congruence and guanxi (personal connections). We also suggest three types of communicative actions critical for trust-building, that is, NN (neutralising the negative), EP (emphasising the positive) and ME (mobilising the emotivity). These actions are realised through engagement and evaluation resources, emotional stories and intimate address forms. We then apply the model to the analysis of trust work in an influential government microblog with the username of @chinapeace. The analysis has the twofold purpose of demonstrating the viability of the model of trust-building discourse and examining the trust work of Chinese government microbloggers facing the pandemic crisis.
Spatial–temporal interaction module for action recognition

Video action recognition methods based on deep learning can be divided into two types: two-dimensional convolutional networks (2D-ConvNets) relied and three-dimensional convolutional networks (3D-ConvNets) relied. 2D-ConvNets are more efficient to learn spatial features, but cannot capture temporal relationships directly. 3D-ConvNets can jointly learn spatial–temporal features, but their learning is time-consuming because of a large number of networks' parameters. We therefore propose an effective spatial–temporal interaction (STI) module. The 2D spatial convolution and the one-dimensional temporal convolution are combined through attention mechanism in STI to learn the spatial–temporal information effectively and efficiently. The computation cost of the proposed method is far less than 3D convolution. The proposed STI module can be combined with 2D-ConvNets to obtain the effect of 3D-ConvNets with far fewer parameters, and it can also be inserted into 3D-ConvNets to improve their ability to learn spatial–temporal features, so as to improve the recognition accuracy. Experimental results show that the proposed method outperforms the existing counterparts on benchmark datasets.
Intelligent Edge Biomedical Sensors in the Internet of Things (IoT) Era

Abstract The design of reliable wearable systems for real-time and long-term monitoring presents major challenges, although they are poised as the next frontier of innovation in the context of Internet-of-Things (IoT) to provide personalized healthcare. This new generation of biomedical sensors targets to be interconnected in ways that improve our lives and transform the medical industry. Therefore, they offer an excellent opportunity to integrate the next generation of artificial intelligence (AI) based techniques in medical devices. However, several key challenges remain in achieving this potential due to the inherent resource-constrained nature of wearable systems for Big Data medical applications, which need to detect pathologies in real time. Concretely, in this chapter, we discuss the opportunities for edge computing and edge AI in next-generation intelligent biomedical sensors in the IoT era and the key challenges in wearable systems design for pathology detection and health/activity monitoring in the context of IoT technologies. First, we introduce the notion of self-awareness toward the conception of the next-generation intelligent edge biomedical sensors to trade-off machine-learning performance versus system lifetime, according to the application requirements of the medical monitoring systems. Subsequently, we present the implications of personalization and multi-parametric sensing in the context of the system-level architecture of intelligent edge biomedical sensors. Thus, they can adapt to the real world, as living organisms do, to operate efficiently according to the target application requirements and available energy at any moment in time. Then, we discuss the impacts of self-awareness and low-power requirements at the circuit level for sampling through a paradigm shift to react to the input signal itself. Finally, we conclude by highlighting that the techniques discussed in this chapter may be applied jointly to design the next-generation intelligent biomedical sensors and systems in the IoT era.
A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification

We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of different races, sexes, etc. Here, we consider bias in the form of group-level differences in false prediction rates between the different sensitive variable groups. Given that the method is fully Bayesian, it is well suited for situations where group parameters or regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on simulated datasets and two real-world datasets.
A comprehensive survey on the ant lion optimiser, variants and applications

The Ant Lion Optimiser (ALO) algorithm is one of the recent nature-inspired algorithms developed by Mirjalili in 2015. This article presents a comprehensive survey on the ALO algorithm that mimics the hunting mechanism of antlions in nature, besides the overview of ALO applications to solve optimisation problems in different fields. The ALO algorithm is simple to use, flexible and scalable and has an adequate balance of exploration and exploitation, making it suitable to find best solution and attaining good convergence. Therefore, the ALO algorithm has gained significant interest among researchers across various domains in a short span. Thus, an updated review of research of the ALO algorithm has been recapitulated involving recent variants, modification and hybridisation that further improved ALO algorithm performance. Initially, introduction of the ALO algorithm is presented covering inspiration and mathematical formulations based on the conceptual framework of antlion behaviour. Furthermore, the optimisation procedure is discussed including the use of all operators involved in five main steps of the algorithm. Finally, various application domains are examined for the ALO algorithm and statistical analysis is performed using Friedman and Wilcoxon rank sum tests. Based on the analysis and investigations performed on this algorithm, it has not only realised a great success in solving numerical optimisation problems but also been implemented in various application areas such as global optimisation, renewable energy, feature selection, power load balancing and image segmentation. Finally, this article ends with conclusion summary and providing several future directions and applications of the ALO algorithm.