Reviewerly/exHarmony huggingface dataset:
Helmet Detection Using YOLO V3 And Single Shot Detector

Wearing a Safety helmet is vital to the well-being of labourers at building destinations and production lines. Instructions to caution or recognize labourers regardless of whether the protective helmet is worn, is regularly a troublesome point for undertakings to screen. This project presents a mechanized system to recognize motor bikers without a helmet and labourers without wearing the helmet. On the helmet identification conundrum, a You Only Look Once (YOLO) model is used. This model will only consider the bounding box territories of construction labourers and motorcyclists using a single structure. Further applying the Single-shot detector (SSD) model to upgrade the prepared pictures likewise perform better in picture limitation because of successful component combination, which is more under the prerequisite of acknowledgement of helmet. The basis of this model is highly critical in terms of enhancing helmet recognition and ensuring secure estimation.
Next Words Prediction Using Recurrent NeuralNetworks

Next Word Prediction is additionally called Language Modeling. It is the undertaking of predicting what word comes straightaway. It is one of the major assignments of NLP and has numerous applications. Attempting to make model utilizing nietzsche default text record which will foresee clients sentence after the clients composed 40 letters, the model will comprehend 40 letters and anticipate impending top 10 words utilizing RNN neural organization which will be executed utilizing Tensorflow. Our Aim of creating this model to predict 10 or more then 10 word as fast as possible utilizing minimum time. As RNN is Long short time memory it will understand past text and predict the words which may be helpful for the user to frame sentences and this technique uses letter to letter prediction means it predict a letter after letter to create a word
RE-DEFINING THE SYSTEM BOUNDARIES OF HUMAN-CENTRED DESIGN

Abstract Most services and products are designed in response to the needs, desires or expectations of humans. A variety of methodologies grouped by the term Human-Centred Design (HCD) have been deployed to formalise and improve this process, ranging from user-centred to participatory practices. However, the approaches’ consideration is primarily limited to individuals in their respective space and time. To examine these system boundaries in detail and address potentials for adaptation, this paper reviews dominant HCD methodologies, categorises them and highlights their respective characteristics. Further, concepts and methodologies from related fields are studied for potential contributions to HCD. This results in a proposed re-definition of the system boundaries of HCD by integrating spatio-temporal impacts on humans through an extended social, environmental and economic scope. The different studied approaches and varying impact assessments are exemplarily applied to the case study of urban mobility, in particular human-centred, scenario-based design approaches. However, the described methods and concepts are kept generic to ensure the applicability across various domains of design practice.
Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning

Human-in-the-loop Machine Learning (HIL-ML) is a widely adopted paradigm for instilling human knowledge in autonomous agents. Many design choices influence the efficiency and effectiveness of such interactive learning processes, particularly the interaction type through which the human teacher may provide feedback. While different interaction types (demonstrations, preferences, etc.) have been proposed and evaluated in the HIL-ML literature, there has been little discussion of how these compare or how they should be selected to best address a particular learning problem. In this survey, we propose an organizing principle for HIL-ML that provides a way to analyze the effects of interaction types on human performance and training data. We also identify open problems in understanding the effects of interaction types.
Multi-Scale Selective Feedback Network with Dual Loss for Real Image Denoising

The feedback mechanism in the human visual system extracts high-level semantics from noisy scenes. It then guides low-level noise removal, which has not been fully explored in image denoising networks based on deep learning. The commonly used fully-supervised network optimizes parameters through paired training data. However, unpaired images without noise-free labels are ubiquitous in the real world. Therefore, we proposed a multi-scale selective feedback network (MSFN) with the dual loss. We allow shallow layers to access valuable contextual information from the following deep layers selectively between two adjacent time steps. Iterative refinement mechanism can remove complex noise from coarse to fine. The dual regression is designed to reconstruct noisy images to establish closed-loop supervision that is training-friendly for unpaired data. We use the dual loss to optimize the primary clean-to-noisy task and the dual noisy-to-clean task simultaneously. Extensive experiments prove that our method achieves state-of-the-art results and shows better adaptability on real-world images than the existing methods.
Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

Over the past few decades, video quality assessment (VQA) has become a valuable research field. The perception of in-the-wild video quality without reference is mainly challenged by hybrid distortions with dynamic variations and the movement of the content. In order to address this barrier, we propose a no-reference video quality assessment (NR-VQA) method that adds the enhanced awareness of dynamic information to the perception of static objects. Specifically, we use convolutional networks with different dimensions to extract low-level static-dynamic fusion features for video clips and subsequently implement alignment, followed by a temporal memory module consisting of recurrent neural networks branches and fully connected (FC) branches to construct feature associations in a time series. Meanwhile, in order to simulate human visual habits, we built a parametric adaptive network structure to obtain the final score. We further validated the proposed method on four datasets (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) to test the generalization ability. Extensive experiments have demonstrated that the proposed method not only outperforms other NR-VQA methods in terms of overall performance of mixed datasets but also achieves competitive performance in individual datasets compared to the existing state-of-the-art methods.
Burnable Pseudo-Identity: A Non-Binding Anonymous Identity Method for Ethereum

The concept of identity has become one common research topic in security and privacy where the real identity of users must be preserved, usually covered by pseudonym identifiers.With the rise of Blockchain-based systems, identities are becoming even more critical than before, mainly due to the immutability property.In fact, many publicly accessible Blockchain networks like Ethereum rely on pseudonymization as a method for identifying subject actions.Pseudonyms are often employed to maintain anonymity, but true anonymity requires unlinkability.Without this property, any attacker can examine the messages sent by a specific pseudonym and learn new information about the holder of this pseudonym.This use of Blockchain collides with regulations because of the right to be forgotten, and Blockchain-based solutions are ensuring that every data stored within the chain will not be modified.In this paper we define a method and a tool for dealing with digital identities within Blockchain environments that are compliant with regulations.The proposed method provides a way to grant digital pseudo identities unlinked to the real identity.This new method uses the benefits of key derivation systems to ensure a non-binding interaction between users and the information model associated with their identity.The proposed method is demonstated in the Ethereum context and illustrated with a case study.
A Decentralized Approach to Education Powered by Blockchain Technology

Recently, some researchers in the field of education have been interested in the applications of Blockchain technology.While some researchers have conducted on the 'academic certificate handling' or 'managing record-keeping' in which are detailed educational administrative sector, others are more interested in reshaping educational landscape based on the key concept of 'decentralization' of Blockchain technology in a larger educational context.Researchers believe that the current centralized learning system or educational model is no longer sustainable, and that the Blockchain technology allows for a total disintermediation and disaggregation of education, specifically in higher education including lifelong learning.In a similar context, this paper will explore how the concept of 'decentralization', which is core concept of Blockchain technology, can transform the landscape of education, specifically focusing on the suggestions of researchers at Knowledge Media Institute at the open university in the UK.In other words, it will investigate how this new paradigm of Blockchain technology can affect on educational domain.To achieve this purpose, firstly, the basic Blockchain technology will be scrutinized, followed by the current major research outcome of educational applications of Blockchain technology will be reviewed.Finally, the implications and future prospects of educational applications of the Blockchain technology will be discussed.
Optimized Training for Convolutional Neural Network Using Enhanced Grey Wolf Optimization Algorithm

Convolutional Neural Networks (CNNs) are widely used in image classification tasks and have achieved significant performance. They have different applications with great success, especially in medical fields. The choice of architecture and hyperparameter settings of the CNN, highly influences both accuracy and its convergence speed. The empirical design and optimization of a new CNN architecture require a lot of expertise and can be very time-consuming. This paper proposes an enhanced Grey Wolf Optimization (GWO) algorithm to efficiently explore a defined space of potentially suitable CNN architectures, and simultaneously optimize their hyperparameters. Moreover, we introduce a spatial resolution reduction for a given image processing task, while taking the skin cancer detection as practical application. Through conducted experiments, we have shown that the obtained results are better than other classification methods in terms of accuracy and convergence speed.
Fault-Tolerant Low-Precision DNNs using Explainable AI

Hardware-efficient machine learning systems deployed in safety-critical systems need to be optimized for two conflicting objectives. One is making neural networks more compact and efficient by techniques such as quantization and pruning. The other is making them robust against faults. Robustness of Deep Neural Networks (DNNs) becomes a challenge in low-bit precision neural networks such as networks quantized with 8-bit integer precision or less and on custom DNN accelerators with emerging memory technologies and techniques such as approximate memory. Errors in the processing of DNNs can be modeled as bit-flips at the software level. These bit-flips can be caused by persistent memory errors, manifesting themselves in corrupted DNN weights, or they can be caused by transient soft errors.In this work, we introduce an open-source fault-injection framework to simulate both persistent errors and transient errors on PyTorch. Subsequently, we propose novel algorithms that utilize explainable AI methods to make DNNs robust against both kinds, i.e., persistent memory errors and transient soft errors, while keeping the overhead small. We show that our approach can be beneficially used here to mitigate (1) persistent errors by identifying important bits in weights and selectively protecting these using error correcting codes, and (2) transient errors by identifying important samples in activations and selectively protecting these using Triple Modular Redundancy (TMR). Our proposed method outperforms the previously known work on selectively protecting DNN bits in terms of performance and creates further opportunities for research in the area of utilizing explainable AI for robustness.
Motion Sickness Lessons from the Southern Ocean

BACKGROUND: The objectives were to assess the prevalence, severity, and medication taken, and to look for predictive factors in order to better identify characteristics of passengers at risk of motion sickness during transport from Hobart in Tasmania to the French polar stations in Antarctica. METHODS: There were 239 passengers who were surveyed over 4 yr with 4 round trips per year using the Motion Sickness Susceptibility Questionnaire (MSSQ), Simulator Sickness Questionnaire (SSQ), state-trait anxiety test (STAI-Trait and STAI-State), and general parameters (age, gender, number of trips, jet-lag, direction of the trip), medication, calculation of the distance of each passengers cabin to the center of gravity (CoG). RESULTS: While the passengers had a low intrinsic sensitivity to motion sickness (MSSQ), 94 reported at least one SSQ symptom of motion sickness, and 38 vomited. Five associated factors were discovered: greater initial sensitivity (MSSQ), anticipation of being ill, younger age, higher level of anxiety at midtrip, and greater distance from the CoG. Of the passengers, there were 54 who took anti-motion sickness medication at different times of the trip, however, these passengers experienced more nausea. This could be due to self-selection since they were more sensitive to motion sickness. CONCLUSION: We identified three predictive factors of motion sickness (greater intrinsic susceptibility, younger age, and greater cabin distance from the CoG). For preventive purposes, two associated factors of MS (anticipation of being ill, MSSQ score) were determined to classify three groups of risk of MS to improve passenger care during the trip. Besnard S, Bois J, Hitier M, Vogt J, Laforet P, Golding JF. Motion sickness lessons from the Southern Ocean. Aerosp Med Hum Perform. 2021; 92(9):720727.
OPTIMIZING LOW-COST UAV AERIAL IMAGE MOSAICING FOR CROP GROWTH MONITORING

Abstract. High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions collect hundreds of photos with high overlap and side overlap in order to generate mosaic without data gaps or distortion. These frames are aligned using the location information associated with each image. The same features are identified in multiple frames for generating the mosaic. In this process, it is common to use all or most of the images which requires a lot of resources. Uploading and processing hundreds of images could take several hours to days. Many farmers and crop consultants in developing countries may not have the necessary resources to upload hundreds of images. This study assessed the optimal number of images required to generate an image mosaic for a crop field without any data gaps or distortion. Images were collected at two different heights and directions. First, the mosaic was generated using all (100%) frames followed by subsets containing 90%, through 50% of images. Results obtained will assist us to plan the settings in future flight missions for acquiring optimal number of images required for generating image mosaic.
Current Problems of Teachers' Readiness of Higher Educational Institutions for Implementing Inclusive Education

The development of world education is implemented in accordance with the Convention on the Rights of Persons with Disability and other regulations that determine the right of people with disability to receive quality education at all levels, including in the field of vocational training. Studying the experience of Ukraine will provide an opportunity to take into account the trends of inclusion in higher education in order to increase its effectiveness. The purpose of the article is to study the state of teachers' readiness of higher educational institutions (HEI) for implementing inclusive education. Methods of research: analysis of literary sources on the problems of inclusive education; questioning teachers of HEI; quantitative and qualitative analysis of the results of the research. The results of the study showed the insufficient readiness of teachers of HEI for implementing inclusive education as evidenced by the lack of understanding of the essence of inclusive education, special educational needs of children and the specifics of their education in inclusive institutions; prejudice against persons with SEN and inclusion processes; low self-esteem of inclusive competence and desire to increase it. According to the results of the questionnaire, the ways of development of inclusion in the HEI were outlined. The perspective task of the research is the development and testing of the content and methods of formation readiness of scientific and pedagogical workers for inclusive education.
Deep Image Prior for Super Resolution of Noisy Image

Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.
Geometry-Guided Dense Perspective Network for Speech-Driven Facial Animation

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A Dataset of Annotated Omnidirectional Videos for Distancing Applications

Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.
Adopting A New Hybrid Force Model: A Survey During Covid-19 In Indian Higher Education

This quantitative study proposes and validates the Hybrid force model by analysing the challenges and opportunities in online education during COVID-19 through an online survey from students and teachers of Indian higher education institutions. The proposed model considers the learner as a system of study and talks about the amalgamation of knowledge, human force (teacher), and techno-logical force for better online learning opportunities and experience. The results show Google Meet, Zoom, and Microsoft Team as the main online platforms for imparting the classes while almost 80% of the students prefer a non-traditional teaching method (online and hybrid). The results highlight that more than 88% of the students prefer to have a personal teaching assistant in their learning trajectory.
Genetic-Based Multi-objective Task Scheduling Algorithm in Cloud Computing Environment

Recently, resource management is the major issues in cloud computing (CC) environment because of dynamic heterogeneity of cloud computing environment.The task scheduling and virtual machines (VMs) allocation play a vital role in resources management of CC.Most of existing works for these issues aim to achieve single objective as maximizing resource utilization, load balancing, or power management.Currently, the big challenge in CC is building task scheduling and virtual machines (VMs) allocation algorithms that consider all these objectives in the same time.This problem is called task scheduling with VMs allocation multi-objectives problem which is NP completeness problem.In this paper, a new task scheduling algorithm is proposed for achieving efficient resource management based on these objectives.This proposed algorithm uses a modified genetic algorithm (GA) to find the optimal solution for choosing the most appropriate VMs for executing received tasks and their appropriate servers that will deploy these VMs.This proposed algorithm uses a matrix structure for representing the chromosome of GAS which combines the ids of tasks, VMs, and servers.Simulation results show that the proposed algorithm achieves better performance than ETVMC, TSACS, and ACO algorithms in terms of makespan, scheduling length, throughput, resource utilization, energy consumption, and imbalance degree.
Nature-inspired metaheuristic scheduling algorithms in cloud: a systematic review

Complex huge-scale scientific applications are simplified by workflow to execute in the cloud environment.The cloud is an emerging concept that effectively executes workflows, but it has a range of issues that must be addressed for it to progress.Workflow scheduling using a nature-inspired metaheuristic algorithm is a recent central theme in the cloud computing paradigm.It is an NP-complete problem that fascinates researchers to explore the optimum solution using swarm intelligence.This is a wide area where researchers work for a long time to find an optimum solution but due to the lack of actual research direction, their objectives become faint.Our systematic and extensive analysis of scheduling approaches involves recently high-cited metaheuristic algorithms like Genetic Algorithms (GA), Whale Search Algorithm (WSA), Ant Colony Optimization (ACO), Bat Algorithm, Artificial Bee Colony (ABC), Cuckoo Algorithm, Firefly Algorithm and Particle Swarm Optimization (PSO).Based on various parameters, we do not only classify them but also furnish a comprehensive striking comparison among them with the hope that our efforts will assist recent researchers to select an appropriate technique for further undiscovered issues.We also draw the attention of present researchers towards some open issues to dig out unexplored areas like energy consumption, reliability and security for considering them as future research work.