Reviewerly/exHarmony huggingface dataset:
Deblurring Method of Face Recognition AI Technology Based on Deep Learning

As a common method of deep learning, a convolutional neural network (CNN) shows excellent performance in face recognition. The features extracted by traditional face recognition methods are greatly influenced by subjective factors and are time-consuming and laborious. In addition, these images are susceptible to illumination, expression, occlusion, posture, and other factors, which bring a lot of interference to the computer face recognition and increase recognition difficulty. Deep learning is the most important technical means in the field of computer vision. The participation of this technology reduces manual participation and can identify the identity of visitors from multiple aspects. This study, based on the introduction at all levels and on the fundamental principle of the colloidal neural network, combines the basic model and the common exploitation methods of aspects to make a model of a combination of multiple aspects. Then, an improved CNN-based multifeature fusion face recognition model is proposed, and the effectiveness of the model in face feature extraction is verified by experiments. With the experimental results, the identification rate for the ORL and Yale data sets is 98.2% and 98.8%, respectively. Accordingly, an online face detection system and recognition system based on the combination of element models are designed. The system can obtain dynamic facial recognition and meet the recognition rate of the design requirements. The system is training four detection models and online recognition, and the test results show that the noise component model has the highest recognition rate, and the recognition rate has improved by 13% compared with the baseline capacity, further verifying that a model of a combination of features can achieve more effectively.
The Physiologic and Emotional Effects of 360-Degree Video Simulation on Head-Mounted Display Versus In-Person Simulation

A key simulation component is its capability to elicit physiological changes, improving recall. The primary aim was to determine whether parasympathetic responses to head-mounted display simulations (HMDs) were noninferior to in-person simulations. The secondary aims explored sympathetic and affective responses and learning effectiveness.The authors conducted a noninferiority trial. Hospital providers who did not use chronotropic medications, have motion sickness, or have seizures were included. The authors randomized participants to in-person or HMD simulation. Biometric sensors collected respiratory sinus arrhythmia and skin conductance levels to measure parasympathetic and sympathetic states at baseline, during, and after the simulation. Affect was measured using a schedule. The authors measured 3-month recall of learning points and used split-plot analysis of variance and Mann-Whitney U tests to analyze.One hundred fifteen participants qualified, and the authors analyzed 56 in each group. Both groups experienced a significant change in mean respiratory sinus arrhythmia from baseline to during and from during to afterward. The difference of change between the groups from baseline to during was 0.134 (95% confidence interval = 0.142 to 0.410, P = 0.339). The difference of change from during the simulation to after was -0.060 (95% confidence interval = -0.337 to 0.217, P = 0.670). Noninferiority was not established for either period. Sympathetic arousal did not occur in either group. Noninferiority was not established for the changes in affect that were demonstrated. The mean scores of teaching effectiveness and achievement scores were not different.Although a parasympathetic and affective response to the video simulation on an HMD did occur, it was not discernibly noninferior to in-person in this study.
Reinforcement Learning Applications for Performance Improvement in Cloud Computing—A Systematic Review

The cloud provides a platform that is easy to access and use the users’ data and applications from anywhere in the world using the Internet. With this fantastic use of the cloud, the number of cloud users has increased exponentially thereby making the cloud sometimes suffer from some of the major issues include resource allotment problems, Virtual Machine problems, etc. Artificial Intelligence is the study that enables the system the ability to think on its own. Artificial Intelligence includes the study of Machine Learning which gives us various statistical tools to explore and help us understand the data. Supervised, Unsupervised, and Reinforcement Learning are the three Machine Learning types. Reinforcement Learning is a type of Machine Learning in which the Machine Learning model uses limited past data and learns with new data. This research paper does the study of the Literature Survey on the different Reinforcement Learning algorithms and how these algorithms can be used to solve the issues of the cloud. The main reason why Reinforcement Learning can be applied to solve the problems of the cloud is that Reinforcement Learning provides automated learning through experience, which is similar to how human beings learn. So hence, if any problem arises on the cloud, it will learn from its past experiences and automatically improve itself to become immune to any arising problem. This technique of learning from experience is different from other Machine Learning techniques because no large data sets are required and this learning is innovative and goal-oriented which enhances itself with the method of rewards. Reinforcement Learning methods and algorithms such as Markov Decision Process, Q-Learning, Double Q-Learning, etc. have made a significant amount of development in the process of learning of the respective agent. The major objective of this research is to implement these Reinforcement Learning algorithms on the cloud to solve some of the issues faced by the cloud. A total of 60 research papers are studied in this research paper, providing a good pool of algorithms and techniques which can be used to solve cloud problems.
EVis: Visually Analyzing Environmentally Driven Events

Earth scientists are increasingly employing time series data with multiple dimensions and high temporal resolution to study the impacts of climate and environmental changes on Earth's atmosphere, biosphere, hydrosphere, and lithosphere. However, the large number of variables and varying time scales of antecedent conditions contributing to natural phenomena hinder scientists from completing more than the most basic analyses. In this paper, we present EVis (Environmental Visualization), a new visual analytics prototype to help scientists analyze and explore recurring environmental events (e.g. rock fracture, landslides, heat waves, floods) and their relationships with high dimensional time series of continuous numeric environmental variables, such as ambient temperature and precipitation. EVis provides coordinated scatterplots, heatmaps, histograms, and RadViz for foundational analyses. These features allow users to interactively examine relationships between events and one, two, three, or more environmental variables. EVis also provides a novel visual analytics approach to allowing users to discover temporally lagging relationships related to antecedent conditions between events and multiple variables, a critical task in Earth sciences. In particular, this latter approach projects multivariate time series onto trajectories in a 2D space using RadViz, and clusters the trajectories for temporal pattern discovery. Our case studies with rock cracking data and interviews with domain experts from a range of sub-disciplines within Earth sciences illustrate the extensive applicability and usefulness of EVis.
Human gesture system in Human Robot Interaction for reliability analysis

Controlling and fastening, under noise and poor lightings conditions, the communication inside the workplace between operators and robots and/or machines is essential to make robust collaborative sets. Humans can control their task by making use of speech and gestures while machine and robot can adapt to human beings by regulating movements, space accessibility and contingencies. This acts on quality and productivity of process in different context (robotic surgery, dis/assembly task, virtual control, augmented training) and un-clustered jobs (as per maintenance actions) maintenance. Safety and reliability are benefitted from robust communication, especially in flexible working environments where there are no physical barriers between human and robots. Due to the dynamic interactions, the complex workplace faces increased uncertainty, which in turn increases the probability of human-system error. In this work, we explore the use - with an integrable, non-invasive, configurable and open access solution - of Machine Learning algorithms paired with Computer Vision to investigate human operators’ fatigue and its possible use in understanding reliability inside the workplace. We propose a solution to integrate gesture recognition in collaborative robotics by controlling robot actions remotely by acting on the bi-direction (stopping robots and/or warning humans) perceived adaptability level with the intent of further investigating reliability in the workplace.
A Tobit traceless Kalman filter technique TUKF: handle truncated data

We propose a Kalman filtering technique that handle truncated data, called Tobit Unscented Kalman Filter (TUKF). Based on description of truncated data, the new TUKF algorithm makes three major corrections to the traceless Kalman filtering algorithm, namely, the correction of the observation equation, the correction of the statistical properties of the measured observations, and the Implemented an extension of the TKF algorithm to UKF algorithm capable of handling. The new TUKF algorithm performs well in the data truncation problem and still maintains good target tracking capability in the detection range-constrained target tracking problem. In addition, the method can be applied not only to linear systems but also to more complex systems such as nonlinear systems.
Identification of Visual Functional Thresholds for Immersion Assessment in Virtual Reality

Abstract We consider that to objectively measure immersion, one needs to assess how each sensory quality is reproduced in a virtual environment. In this perspective, we introduce the concept of functional threshold which corresponds to the value at which a sensory quality can be degraded without being noticed by the user of a virtual environment. We suggest that the perceived realism of a virtual experience can potentially be evoked for sensory qualities values ranging from the perceptual threshold to the functional threshold. Thus, the identification of functional thresholds values allows us to constrain immersion. To lay the foundation for the identification of functional thresholds, we applied a modified version of the method of limits. We measured the value at which 30 participants were able to identify the degradation of their field of view (FOV), visual acuity, and contrast sensitivity while executing a multidirectional selection test. This enabled us to identify functional perceptual thresholds of 96.6 degrees for FOV, 12.2 arcmin for visual acuity, and 25.6% for contrast sensitivity.
An intrinsically-typed solution for the list-machine benchmark

Formal models are important tools in the programming language research community. However, such models are full of intricacies and, due to that, they are subject to subtle errors. Such failures motivated the usage of tools to ensure the correctness of these formalisms. One way to eliminate such errors is to encode models in a dependently-typed language in order to ensure its “correctness-by-construction”. In this paper, we use this idea to build a verified interpreter for the list-machine benchmark in the Agda programming language, comparing the results with formalizations developed by Appel et al.. We formalize the 14 tasks of the benchmark using roughly 14% of LOC compared to a Twelf solution, and 47% of LOC compared to a Coq solution, even without the use of proof automation. We also describe a solution to the second version of the benchmark and compare it with Appel’s et. al. Coq-based solution.
Partial Knowledge Transfer in Visual Recognition Systems via Joint Loss-Aware Consistency Learning

One of the key challenges for the implementation of visual recognition systems in the real world is to construct prediction models that can realize the knowledge transfer from the seen data to the unseen data. Specifically, partial knowledge transfer (PKT) aims to address a more common and realistic scenario in which we are accessible to a label-rich source domain while working on a relative label-scarce target domain. The essence of PKT is to explore the latent categories across different domains and simultaneously facilitate the positive transfer from these data. In this article, we propose a joint loss-aware consistency learning (JLACL) to effectively enhance the transferability of knowledge in visual recognition systems, which conducts an iterative optimization on three-level losses, including discrepancy loss, consensus loss, and cross-entropy loss. The discrepancy loss is designed to eliminate the class distribution bias by a similarity perception metric between the source and target domains. The consensus loss can assist to preserve domain-invariant and representative features for model learning by exploring correlation. Moreover, we also find that using the cross-entropy loss to determine the shared label space, which can help to alleviate the negative transfer by suppressing the features with nonshared labels. Finally, the PKT can be successfully achieved by joint optimization of total losses. Extensive experiments on several public and challenging datasets in visual recognition applications adequately demonstrate the superiority of our proposed JLACL over existing state-of-the-art PKT methods.
Estimation in Agile Software Development Using Artificial Intelligence

In software development, agile methods are becoming more popular, and in many situations, software development teams are even mandated to use some agile methods in their projects. It is basic to give as exact a gauge as could really be expected. Today, in the data innovation business, for assessment in spry programming advancement, most of the part is dependent on heuristic methodologies like master judgment and arranging poker. It is very hard to gauge coordinated programming advancement without nimble mastery. Various studies have been done throughout the years to evaluate software effort estimating methodologies, but because of the rise of new software development processes, the reviews have not been caught up with them. This article gives an intensive assessment of cost assessment in agile software development, which will help the clients in understanding current expense assessment drifts in ASD. Most agile teams estimate software development effort using expert estimating methodologies, according to a thorough literature analysis and survey. This work includes a thorough literature review that has been updated by examining data from 73 new studies. The majority of the data comes from single-company databases; however, cross-company data is extremely popular. Estimates of effort and cost are typically based on the findings of a study using models or historical data applied to size, activities, and other planning characteristics.
Human Posture Recognition Based on Bidirectional Long Short-Term Memory Neural Network

Sensor-based human posture recognition is an important research direction in the field of pattern recognition and plays an important role in many fields. The extracted features will directly affect the accuracy of pattern recognition. In this paper, the posture recognition method based on a Bidirectional Long Short-Term Memory neural network (Bi-LSTM) is designed and validated, the neural network is adopted in this algorithm for extracting features instead of manual extraction. Finally, the accuracy of the algorithm is verified by experimental data. The results show that the posture recognition algorithm constructed by the Bi-LSTM has a high recognition accuracy.
Secure Data Sharing of Electronic Health Record (EHR) on the Cloud Using Blockchain in Covid-19 Scenario

In the current scenario of Covid-19, health data are stored in a centralized server at hospitals and medical institutions, which is susceptible to diverse extortions, such as malicious tinkering, patient data loss, and natural disasters. The advent of blockchain expertise fetches a new-fangled notion to decipher the abovementioned security issues due to its features of immutability, verifiability, and decentralization. This technology was initially invented for money-associated exchanges and financial transactions using centralized authorities. Nowadays, the popularity of blockchain technology is increasing enormously in the field of healthcare. Healthcare devices having a great utilization in the field of healthcare, but they pose several privacy and security threats to the sensitive data of a patient. To overcome these issues, we have proposed a novel hybrid system that combines the merits of centralized and decentralized blockchain for the secure sharing of health data between health devices and hospitals. The proposed system will be useful in the context of protected, interoperable, and effectual admittance to health registers by patients, hospitals, and third revelries while maintaining the privacy of patients’ penetrating data. Also, we used smart contracts in an Ethereum-based blockchain for access control. Ethereum is a programmable blockchain platform that utilizes the strong environment of solidity (a state-based scripting language). By using both centralized and decentralized properties of blockchain together, we can effectively secure the patient's sensitive data. The data of a patient generated by heterogeneous devices would be shared in a secured manner. We have analyzed that the proposed system is better than the existing systems in terms of scalability, data ownership, and interoperability. The deployment cost of the proposed hybrid model may be the limitation. In the forthcoming, we would implement the suggested hybrid exemplary to advance the security of health data.
The Conditional Super Learner

Using cross validation to select the best model from a library is standard practice in machine learning. Similarly, meta learning is a widely used technique where models previously developed are combined (mainly linearly) with the expectation of improving performance with respect to individual models. In this article we consider the Conditional Super Learner (CSL), an algorithm that selects the best model candidate from a library of models conditional on the covariates. The CSL expands the idea of using cross validation to select the best model and merges it with meta learning. We propose an optimization algorithm that finds a local minimum to the problem posed and proves that it converges at a rate faster than Op(n-1/4). We offer empirical evidence that: (1) CSL is an excellent candidate to substitute stacking and (2) CLS is suitable for the analysis of Hierarchical problems. Additionally, implications for global interpretability are emphasized.
Deep Learning-Based Cryptanalysis of Different AES Modes of Operation

With the advent of machine learning and the development of powerful machines, the problem of decryption takes on a new light and opens new avenues for research. The remarkable rise of technologies and algorithms contributes to the widespread use of machine learning in various cases. One of the uses is in cryptanalysis and attack of algorithms used in cryptographic processes. In this paper, we elaborate the idea by using the deep neural network to perform the known-plaintext attack on AES to restore as many bits as possible, on the given plaintext. Moreover, we perform our experiments on different key sizes and different modes of operation on AES. The results show that the deep neural network can restore the bits in the whole data set with a probability of more than 98%, restore two consecutive bytes with more than 70%, and more than half of the plaintext bytes with a probability of 99%.
Light-OCB: Parallel Lightweight Authenticated Cipher with Full Security

This paper proposes a lightweight authenticated encryption (AE) scheme, called Light-OCB, which can be viewed as a lighter variant of the CAESAR winner OCB as well as a faster variant of the high profile NIST LWC competition submission LOCUS-AEAD. Light-OCB is structurally similar to LOCUS-AEAD and uses a nonce-based derived key that provides optimal security, and short-tweak tweakable blockcipher (tBC) for efficient domain separation. Light-OCB improves over LOCUS-AEAD by reducing the number of primitive calls, and thereby significantly optimizing the throughput. To establish our claim, we provide FPGA hardware implementation details and benchmark for Light-OCB against LOCUS-AEAD and several other well-known AEs. The implementation results depict that, when instantiated with the tBC TweGIFT64, Light-OCB achieves an extremely low hardware footprint - consuming only around 1128 LUTs and 307 slices (significantly lower than that for LOCUS-AEAD) while maintaining a throughput of 880 Mbps, which is almost twice that of LOCUS-AEAD. To the best of our knowledge, this figure is significantly better than all the known implementation results of other lightweight ciphers with parallel structures.
Blockchain Technology and Organizational Practices: The Case of Nigerian Academic Libraries

Blockchain technology transforms organizational functions in this modern time. Notwithstanding the embryonic stage, its potentials concerning organizational practices are of interest, as it resolves numerous issues confronting achieving global best practices. It identifies library practices requiring application of blockchain technology. It discusses the extent blockchain technology that enhances library practices and identifies challenges with blockchain technologies in libraries. Study population consists of 133 professional librarians. A descriptive survey design was adopted. Questionnaire and focus group discussion were used for data collection. A total of 115 questionnaires were retrieved from 133 administered, giving a response rate of 86.5%. Library practices like resource sharing ( $$ \overline{\mathrm{X}} $$ = 3.06), protection of copyright ( $$ \overline{\mathrm{X}} $$ = 3.05), and record management ( $$ \overline{\mathrm{X}} $$ = 2.92) could be enhanced with blockchain technology. Poor funding ( $$ \overline{\mathrm{X}} $$ =2.98), lack of skill technicians ( $$ \overline{\mathrm{X}} $$ =2.99), and poor broadband connectivity ( $$ \overline{\mathrm{X}} $$ = 2.84) were identified as challenges. There is a need to encourage adoption of blockchain technology since it improves library activities.