Reviewerly/exHarmony huggingface dataset:
Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Our study aims to review and analyze the most relevant studies in the image dehazing field.Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature.These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature.A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies.The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021.These indices are selected because they are sufficient in terms of coverage.Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set.A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews.Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles.Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions.Many relevant studies have been conducted to meet these critical requirements.We conducted objective image quality assessment experimental comparison of various image dehazing algorithms.In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas.We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing.
Causality in digital medicine

Abstract Ben Glocker (an expert in machine learning for medical imaging, Imperial College London), Mirco Musolesi (a data science and digital health expert, University College London), Jonathan Richens (an expert in diagnostic machine learning models, Babylon Health) and Caroline Uhler (a computational biology expert, MIT) talked to Nature Communications about their research interests in causality inference and how this can provide a robust framework for digital medicine studies and their implementation, across different fields of application.
The systematic evaluation of an embodied control interface for virtual reality

Embodied interfaces are promising for virtual reality (VR) because they can improve immersion and reduce simulator sickness compared to more traditional handheld interfaces (e.g., gamepads). We present a novel embodied interface called the Limbic Chair. The chair is composed of two separate shells that allow the user's legs to move independently while sitting. We demonstrate the suitability of the Limbic Chair in two VR scenarios: city navigation and flight simulation. We compare the Limbic Chair to a gamepad using performance measures (i.e., time and accuracy), head movements, body sway, and standard questionnaires for measuring presence, usability, workload, and simulator sickness. In the city navigation scenario, the gamepad was associated with better presence, usability, and workload scores. In the flight simulation scenario, the chair was associated with less body sway (i.e., less simulator sickness) and fewer head movements but also slower performance and higher workload. In all other comparisons, the Limbic Chair and gamepad were similar, showing the promise of the Chair for replacing some control functions traditionally executed using handheld devices.
A novel adaptive moving average method for signal denoising in strong noise background

The moving average (MA) method has been widely used in signal processing, but it has problems of the dead zone and the fixed window. In this paper, an adaptive moving average (AMA) filtering method is proposed, which can sniff the inherent characteristics of the signal and assign time-varying optimal parameters to signal processing, hence solve dead zone and the fixed window problems. Firstly, this paper builds the theoretical framework of AMA, including the trial steps and optimization of the necessary parameters. To verify the effectiveness of the AMA, three signal processing methods are taken as comparison methods to process the noisy simulation and experimental signals. Comparison methods includes the MA, variational mode decomposition (VMD), and wavelet threshold denoising (WTD). Signals processed include linear frequency modulation (LFM) simulation signal, aperiodic square wave (ASW) simulation signal, LFM experimental signal produced by a signal generator, and nondestructive test signal of wire rope. Also, the output is analyzed qualitatively and quantitatively with signal-to-noise ratio (SNR), cross-correlation coefficient, amplitude error, and a newly defined local coincidence index. Compared to MA, VMD and WTD, the proposed AMA can solve the dead zone problem, recover noisy signal with higher SNR, cross-correlation coefficient, and lower amplitude error. These results indicate that AMA is a promising method in signal processing.
Robust Real-Time Traffic Surveillance with Deep Learning

Real-time vehicle monitoring in highways, roads, and streets may provide useful data both for infrastructure planning and for traffic management in general. Even though it is a classic research area in computer vision, advances in neural networks for object detection and classification, especially in the last years, made this area even more appealing due to the effectiveness of these methods. This study presents TrafficSensor, a system that employs deep learning techniques for automatic vehicle tracking and classification on highways using a calibrated and fixed camera. A new traffic image dataset was created to train the models, which includes real traffic images in poor lightning or weather conditions and low-resolution images. The proposed system consists mainly of two modules, first one responsible of vehicle detection and classification and a second one for vehicle tracking. For the first module, several neural models were tested and objectively compared, and finally, the YOLOv3 and YOLOv4-based network trained on the new traffic dataset were selected. The second module combines a simple spatial association algorithm with a more sophisticated KLT (Kanade-Lucas-Tomasi) tracker to follow the vehicles on the road. Several experiments have been conducted on challenging traffic videos in order to validate the system with real data. Experimental results show that the proposed system is able to successfully detect, track, and classify vehicles traveling on a highway on real time.
Allocation method of communication interference resource based on deep reinforcement learning of maximum policy entropy

In order to solve the optimization of the interference resource allocation in communication network countermeasures, an interference resource allocation method based on the maximum policy entropy deep reinforcement learning (MPEDRL) was proposed. The method introduced the idea of deep reinforcement learning into the communication countermeasures resource allocation, it could enhance the exploration of the policy and accelerate the convergence to the global optimum with adding the maximum policy entropy criterion and adaptively adjusting the entropy coefficient. The method modeled interference resource allocation as Markov decision process, then established the interference strategy network to output allocation scheme, constructing the interference effect evaluation network of the clipped twin structure for efficiency evaluation, and trained the policy network and the evaluation network with the goal of maximizing the strategy entropy and the cumulative interference efficacy, then decided the optimal interference resource allocation scheme. The simulation results show that the algorithm can effectively solve the resource allocation problem in communication network confrontation, comparing with the existing deep reinforcement learning methods, it has faster learning speed and less fluctuation in the training process, and achieved 15% higher jamming efficacy than DDPG-based method.
E-governance using mobile applications: A case study of India during the COVID-19 pandemic

Healthcare initiatives backed by electronic-governance (e-governance) principles have contributed well to the extant literature and practice. Governments and healthcare systems across the world were taken aback by the swamping impact of the COVID-19 pandemic. However, they reacted quickly by developing contact-tracing mobile applications (apps) for creating awareness, providing information about various healthcare initiatives, and helping citizens to use the required information in case of emergency. The major challenge was to develop such e-governance interventions in a short time and ensure their quick adoption among the masses. Hence, it is worthwhile to investigate the factors leading to the adoption of such e-governance initiatives, especially in the context of a widespread pandemic situation. The present study is an attempt to analyze the factors driving the intention to use contact tracing mobile apps launched by governments globally during the COVID-19 pandemic. We have conducted the study in the context of India, where the government launched a community-driven contact tracing mobile app for its citizens during the COVID-19 pandemic in April 2020. The study adopted an empirical approach to test how epistemic value, convenience value, conditional value, functional value, and privacy concerns influenced the intention to use this approach. The study found that intention to use such an app was positively influenced by functional value, which in turn was positively influenced by convenience and conditional values. It suggests that the convenience of using the app, perceived seriousness of the pandemic (i.e., conditional value), and utilitarian benefits (i.e., functional value) of the contact-tracing mobile app enhanced its acceptance. However, its novelty (i.e., epistemic value) and privacy concerns are not significant predictors of intention to use. The study recommends that the government should place more emphasis on improving the functional value which is driven by convenience and context-specific features to push the use of an e-governance initiative during the crisis.
Intra- and Interorganizational Barriers to Blockchain Adoption: A General Assessment and Coping Strategies in the Agrifood Industry

Background: Companies partaking in modern supply chains face numerous intra- and interorganizational barriers when it comes to the adoption of blockchain technology. Empirical research is missing that explores how exactly these barriers can be overcome. In this paper we first explore barriers that organizations need to overcome to successfully deploy blockchain technology. In a second step, we investigate the agrifood industry and highlight differences in coping strategies between incumbents and start-ups. Methods: We conducted a quantitative survey with 190 supply chain experts to identify barriers and an in-depth qualitative study that included 10 expert interviews to better understand the current situation in agrifood organizations. Results: The findings from the quantitative study show that the most relevant organizational barrier to blockchain adoption is the widespread lack of understanding of the technology and its potential benefits. In the qualitative study we illustrate how various intra- and interorganizational barriers can be overcome and how the resources and capabilities differ between incumbents and start-ups. Conclusions: Our results provide academics with a better understanding of the relevant barriers and bridges of blockchain adoption. Practitioners benefit from learning about the resources and capabilities they need to deploy in order to benefit from blockchain technology.
Value Iteration Networks with Double Estimator for Planetary Rover Path Planning

Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN suffers from several limitations, including sensitivity to initialization and poor performance in large-scale domains. We introduce the double value iteration network (dVIN), which decouples action selection and value estimation in the VI module, using the weighted double estimator method to approximate the maximum expected value, instead of maximizing over the estimated action value. We have devised a simple, yet effective, two-stage training strategy for VI-based models to address the problem of high computational cost and poor performance in large-size domains. We evaluate the dVIN on planning problems in grid-world domains and realistic datasets, generated from terrain images of a moon landscape. We show that our dVIN empirically outperforms the baseline methods and generalize better to large-scale environments.
Knowledge management process, knowledge based innovation: Does academic researcher’s productivity mediate during the pandemic of covid-19?

Drucker's knowledge-worker productivity theory and knowledge-based view of the firm theory are widely employed in many disciplines but there is little application of these theories in knowledge-based innovation among academic researchers. Therefore, this study intends to evaluate the effects of the knowledge management process on knowledge-based innovation alongside with mediating role of Malaysian academic researchers' productivity during the Pandemic of COVID-19. Using a random sampling technique, data was collected from 382 academic researchers. Questionnaires were self-administered and data was analyzed via Smart PLS-SEM. Knowledge management process and knowledge workers' productivity have a positive and significant relationship with the knowledge-based innovation among academic researchers during the Pandemic of COVID-19. In addition, knowledge workers' productivity mediates the relationship between the knowledge management process (knowledge creation, knowledge acquisition, knowledge sharing, and knowledge utilization) and knowledge-based innovation during the Pandemic of COVID-19. Results have also directed knowledge sharing as the key factor in knowledge-based innovation and a stimulating task for management discipline around the world during the Pandemic of COVID-19. This study provides interesting insights on Malaysian academic researchers' productivity by evaluating the effects of knowledge creation, acquisition, sharing, and application on the knowledge-based innovation among academic researchers during the Pandemic of COVID-19. These useful insights would enable policymakers to develop more influential educational strategies. By assimilating the literature of defined variables, the main contribution of this study is the evaluation of knowledge creation, acquisition, sharing, and utilization into knowledge-based innovation alongside the mediating role of knowledge workers productivity in the higher education sector of Malaysia during the Pandemic of COVID-19.
The Cyber Threat Landscape in South Africa: A 10-Year Review

The world is witnessing a rise in cyber-related incidents. As information technology improves and the reliance on technology increases, the frequency and severity of cyber incidents escalate. The impact is felt globally, and South Africa is not immune to the effects. The country’s fast-paced technological evolution continues to increase the attack surface within the cyber domain. The increased attack surface is confirmed by recent cyberattacks affecting well-known and established South African organisations. This article reviews findings from an evaluation of South Africa’s cyber threat landscape that analysed 74 cyber incidents identified as occurring between 2010 and 2020. The 74 incidents are categorised according to incident type, affected sector, perpetrator type, and motivation. It is found that the most common incident type is data exposure, the most-affected sector is the public sector, the most prevalent perpetrators are hackers, and the most common motivation is criminal. The article makes recommendations about how South Africa can reduce the risk factors in its cyber threat landscape.
Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study

Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174-174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.
Yoga Pose Perfection using Deep Learning

Abstract&#x0D; Even with lots of attention and work in the computer vision and artificial intelligence field, human body pose detection is still a daunting task. The application of human pose detection is wide-ranging from health monitoring to public security. This paper focuses on the application in yoga, an art that has been performed for over a millennium. In modern society yoga has become a common method of exercise and there-in arises a demand for instructions on how to do yoga properly. Doing certain yoga postures improperly may lead to injuries and fatigue and hence the presence of a trainer becomes important. As many people don’t have the resources to have a yoga instructor or guide, artificial intelligence can act as a substitute and advise people on their poses. Currently, the research surrounding pose estimation for yoga mainly discusses the classification of yogic poses. In this work, we propose a method, using the Tensorflow MoveNet Thunder model, that allows real-time pose estimation to detect the error in a person's pose, thereby allowing them to correct it.
The Mechanism of Evolution and Balance for e-Commerce Ecosystem under Blockchain

With the development of society, e-commerce competition has become increasingly intense and has ascended to the level of the ecosystem. Therefore, it is extremely significant to study the mechanism of evolution and balance for the e-commerce ecosystem. Simultaneously, blockchain technology is essentially a consensus mechanism, the core idea of which is decentralization, but it is actually the deconstruction of privileges and authority. Especially, the influence on the e-commerce ecosystem cannot be underestimated. Blockchain technology ultimately changes not only technology, but a comprehensive reconstruction of various industries. Building an e-commerce information ecosystem based on blockchain can promote the healthy and sustainable development of e-commerce information ecology. This work combines the definition and technical characteristics of blockchain, discusses the blockchain-based e-commerce information ecosystem model, and discusses how to achieve the ecological balance and system evolution of e-commerce under the background of blockchain. According to the internal problems of the e-commerce ecosystem, three evolutionary paths are proposed in this work. First, consider the timeliness of the information and construct a full-process information channel. Second, remove central nodes and build a safe and efficient block payment. Third, solve the blind zone in the field of logistics and create efficient and transparent intelligent logistics. This work can provide an effective reference for the development of e-commerce.
HCNET: A Point Cloud Object Detection Network Based on Height and Channel Attention

The use of LiDAR point clouds for accurate three-dimensional perception is crucial for realizing high-level autonomous driving systems. Upon considering the drawbacks of the current point cloud object-detection algorithms, this paper proposes HCNet, an algorithm that combines an attention mechanism with adaptive adjustment, starting from feature fusion and overcoming the sparse and uneven distribution of point clouds. Inspired by the basic idea of an attention mechanism, a feature-fusion structure HC module with height attention and channel attention, weighted in parallel, is proposed to perform feature-fusion on multiple pseudo images. The use of several weighting mechanisms enhances the ability of feature-information expression. Additionally, we designed an adaptively adjusted detection head that also overcomes the sparsity of the point cloud from the perspective of original information fusion. It reduces the interference caused by the uneven distribution of the point cloud from the perspective of adaptive adjustment. The results show that our HCNet has better accuracy than other one-stage-network or even two-stage-network RCNNs under some evaluation detection metrics. Additionally, it has a detection rate of 30FPS. Especially for hard samples, the algorithm in this paper has better detection performance than many existing algorithms.