Reviewerly/exHarmony huggingface dataset:
Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks

In recent years, the hazy weather in China occurs frequently, and image dehazing has gradually become a research hotspot. To improve the dehazing effect of the hazy images, this paper has proposed a multilevel image dehazing algorithm using conditional generative adversarial networks (CGAN). The hazy image is used to generate the composed image <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> jointly estimated by a transmission map and atmospheric light value through a generator network, and a dehazed image is calculated through an improved atmospheric scattering model. The generator network and the joint discriminator network are subjected to adversarial training and reconstruction constraints. The experimental results show that the proposed method achieved good dehazing effect in synthetic hazy images and real hazy images, and is ahead of other advanced dehazing methods in subjective evaluation and objective evaluation.
The role of information culture in the formation of a cultural ideal

Е. В. Грязнова, И. А. Треушников, А
A new whale optimisation algorithm based on self-adapting parameter adjustment and mix mutation strategy

The Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales, is a recently developed meta-heuristic algorithm. However, WOA exists the defect of easily falling into local optimum. This paper proposes a new WOA based on a self-adapting parameter adjustment and a mix mutation strategy (abbreviated as SMWOA). The self-adapting parameter adjustment strategy based on a normal distribution is adopted to make the algorithm jump out of local optima and enhance the global exploration capability. Meanwhile, in order to obtain a better tradeoff between the exploration and exploitation capabilities of the WOA, a novel mix mutation strategy is embedded in the proposed algorithm. The performance of SMWOA is tested on 23 benchmark functions and test suites composed of four engineering design problems. Experimental results and statistical analyses indicate that the proposed algorithm is very competitive when compared to the original WOA as well as some state-of-the-art algorithms.
Context awareness architecture for ambient-assisted living applications: Case study of nighttime wandering

ObjectivesThis work presents an ambient-assisted living application that encourages seniors during nocturnal wandering episodes to return to bed in calm and comfort reassurance. MethodsStructuring knowledge by designing a software architecture capable of delivering high-level analysis and processing. A senior’s home has been upgraded into a smart home enabling the gathering of habits for two weeks and set up for personalized assistance over four weeks. Home automation devices associated with Actigraph monitors and self-reported sleep were used for more accuracy. ResultsThe architectural model can be used in ambient-assisted living applications for which data collection is permanent and continuous. Its layered organization facilitates the management of specific and general activities of daily life. The results of the home experience show that the system gave a notification whenever the need arose. On the other hand, it allowed the caregiver to get more information about the lifestyle of the senior. ConclusionsFuture work should focus on providing more services to contextualize assistance. Ontology is used to structure all the ambient knowledge of the smart home. We also plan to do more home experiments.
Hole-Filling of RealSense Depth Images Using a Color Edge Map

The RealSense camera frequently includes hole regions-unfilled regions where depth information are missing in the depth image.The holes cause serious problems in applications that use depth sensors and often straddle the background and object simultaneously.Accordingly, a hole must be filled by considering the boundary of the object.Fortunately, the object boundary inside the hole can be estimated by the RealSense color image.This paper proposes a hole-filling method in which filling converges to the object boundary from both border pixels.However, the color edge elements of the object boundary are often missing.Because the missing edge highly influences the hole-filling procedure, the boundary of the object close to the camera should be filled as completely as possible.Consequently, we introduce a simple method of filling the gap between two edge endpoints.Furthermore, we can address the situation in which there are one or more edge pixels between two border pixels.We repeatedly change the filling direction by alternating between the horizontal and vertical directions until no hole remains.Our experiment reveals that the proposed method outperforms other filling methods when numerically comparing the hole-filled depth image with the ground truth image for four methods: root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index map (SSIM), and correlation coefficient (CC).The proposed method also produces a superior visual when we observe the 3D stereo image pair constructed using an original color image and a left-view color image generated with depth-image-based rendering (DIBR).
Using a 22-layer U-Net to perform segmentation of squamous cell carcinoma on digitized head and neck histological images

Squamous cell carcinoma (SCC) comprises over 90 percent of tumors in the head and neck. The diagnosis process involves performing surgical resection of tissue and creating histological slides from the removed tissue. Pathologists detect SCC in histology slides, and may fail to correctly identify tumor regions within the slides. In this study, a dataset of patches extracted from 200 digitized histological images from 84 head and neck SCC patients was used to train, validate and test the segmentation performance of a fully-convolutional U-Net architecture. The neural network achieved a pixel-level segmentation AUC of 0.89 on the testing group. The average segmentation time for whole slide images was 72 seconds. The training, validation, and testing process in this experiment produces a model that has the potential to help segment SCC images in histological images with improved speed and accuracy compared to the manual segmentation process performed by pathologists.
Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears

Malaria, caused by <i>Plasmodium</i> parasites, continues to be a major burden on global health. <i>Plasmodium falciparum</i> (<i>P. falciparum</i>) and <i>Plasmodium vivax</i> (<i>P. vivax</i>) pose the greatest health threat among the five malaria species. Microscopy examination is considered as the gold standard for malaria diagnosis, but it requires a significant amount of time and expertise. In particular, the automated and accurate detection of <i>P. vivax</i> is difficult due to the low parasitemia levels as compared to <i>P. falciparum</i>. In this work, we develop a rapid and robust diagnosis system for the automated detection of <i>P. vivax</i> parasites using a cascaded YOLO model. This system consists of a YOLOv2 model and a classifier for hardnegative mining. Results from 2567 thin blood smear images of 171 patients show the cascaded YOLO model improves the mean average precision about 8% compared to the conventional YOLOv2 model.
User Experience Evaluation through Automatic A/B Testing

The goal of this research is to develop an A/B testing method to automatically compare the user experience (UX) of alternative designs for a web application in a real context with a large number of users. The challenge that it poses is to find mechanisms to predict the UX with machine learning techniques. This submission outlines the motivation, research goal, current status and remaining work.
ExerTrack—Towards Smart Surfaces to Track Exercises

The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or cycling can be accurately recognized using wearable devices. However whole-body exercises such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for recognizing whole-body activities. Computer vision techniques on image data also report high recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas. Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises with a user-independent recognition accuracy of 93.5% and a user-dependent recognition accuracy of 95.1% based on a test study with 9 participants each performing 2 full sessions. We adopt a template-based approach to count repetitions and reach a user-independent counting accuracy of 93.6%. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of our proposed system and model selection to improve the recognition accuracy and data augmentation technique to regularize the network.
Lyapunov Control of High-Dimensional Closed Quantum Systems Based on Particle Swarm Optimization

For high-dimensional closed quantum systems, this paper proposes a novel quantum Lyapunov control scheme based on the particle swarm optimization algorithm and achieves a high-probability population transfer of the system to a non-isolated target eigenstate in the LaSalle invariant set under usual smooth Lyapunov control laws. Via a quadratic Lyapunov function with unknown parameters, a control law with the unknown parameters is designed; based on the LaSalle invariance principle and the energy-level connectivity graph, the stability of the system is analyzed; by using the particle swarm optimization algorithm, a set of optimal parameters is obtained to achieve the control goal. In particular, we propose a path planning method based on the energy-level connectivity graph to determine the initial values of the unknown parameters, which is such that the optimization algorithm can efficiently and conveniently find a set of optimal solutions of the unknown parameters. Numerical simulation experiments on a five-level quantum system and a three-qubit system demonstrate that the proposed Lyapunov control scheme based on the particle swarm optimization algorithm has a good control effect.
A Crisis Situations Decision-Making Systems Software Development Process With Rescue Experiences

Previously, we have proposed a computational model for decision-making in crisis situations called C-RPD (Computational Recognition Primed Decision). In this paper, a software development process customized for Crisis Situations Decision-Making Systems (CSDMSs) is proposed. Agile processes can skillfully manage uncertainty in software requirements and some of their features like incremental development can solve some problems in developing CSDMSs. However, these processes do not provide comprehensive solutions for issues like the lack of enough knowledge about CSDMSs, very rapid changes, urgent need to overcome security challenges, high development unpredictability, and the performance test. Extreme Programming (XP) is one of the best and most widely-used agile processes. In this article, a customized version of XP called Crisis Situations Decision-Making Systems Software Development Process (CSDP) is proposed. Standing first and second in five national and international RoboCup rescue agent simulation tournaments from 2006 to 2010 bear witness to the efficiency of the developed software using CSDP. Relying on its characteristics, CSDP has been able to practically tackle the challenges of developing CSDMSs such as the lack of crisis-related knowledge and cumulative nature of crisis-related knowledge, difficulty of extracting knowledge, long development cycle, and sudden and frequent changes in system requirements.
Initial Coin Offerings (ICOs): benefits, risks and success measures

The initial coin offering is usually defined as a new, innovative way of raising funds used by organizations, companies, or entrepreneurs, attracting funds through a cryptographic exchange in exchange for the coins that can be sold, used for future products or services purchase or profit.This article provides an overview of the evolving ICO market and indicates the main advantages and risks of the ICO.It is pointed out that ICO offers many advantages, such as high liquidity, return on investment, availability, fast capital raising process, minimization of capital raising costs.Nonetheless, the noticeable benefits of ICOs are often available only when companies or investors agree to take considerable risks, caused by the information asymmetry, lack of investor protection, proper regulation and methodical guidance both for investors and entrepreneurs.Considering the novelty, volatility, emerging risks, growing popularity of the ICO market and the lack of research in the scientific literature, it is important to investigate the factors that influence the success of the ICO, which is usually defined as the amount of funds attracted, listing of the coins, return on investment, market capitalization or the duration of the ICO.
SMARTPHONE SECURITY AWARENESS, PERCEPTIONS AND PRACTICES: A WELSH HIGHER EDUCATION CASE STUDY

D.J. Cranfield1, I.M. Venter2, R.J. Blignaut2, K. Renaud3 1University of Swansea (UNITED KINGDOM) 2University of the Western Cape (SOUTH AFRICA) 3Abertay University (UNITED KINGDOM)
Development of B D F-AR 2 (Physics Digital Book Based Augmented Reality) to train students' scientific literacy on Global Warming Material

There have been many developments in digital books, but they are different from BDF-AR2 (Physics Digital Book Based Augmented Reality), this is because the developed BDF-AR2 has features that can train the literacy skills of learners including Augmented Reality (AR) feature so that learning can be more real. The main objective of this research is to describe eligibility (validity, practicality, and effectiveness) of BDF-AR2 media that has been developed This research employed Hannafin &amp; Peck Model development design using by using instruments of media validation, learning completion and validated test instrument. Media validation was carried out by three media experts and resulted in the media B D F-AR 2 (Physics Digital Book Based Augmented Reality) on the global warming material is classified as very valid (93.5%). As for practicality (use BDF-AR2 media in learning) is obtained 83.29% and categorized very good. While for effectiveness (students' scientific literacy skills), it was found that students' scientific literacy skills for 36.67% of students are in a good category, 56.67% of student have an average level and 6.96% of students had poor category for literacy competence science in explaining the phenomenon. As for scientific literacy competencies in interpreting data and evidence scientifically, 30% students have good grades, 66.67% had a fair grade and 3.33 had a poor grade. Referring to the results of validity, practicality, and effectiveness of use BDF-AR2 in learning, it can be concluded that the developed BDF-AR2 is feasible to use in learning. The implication of this research is to present a media that supports 21st-century learning that has been proclaimed by the government.
Approaches and Trends of Automatic Bangla Text Summarization

As long as the internet user is increasing, online electronic content is growing proportionally irrespective of languages. A lot of research works on English text summarization have come to light to deal with this gigantic body of online text. Unfortunately, a few works have been accomplished for Bangla though a huge number of people are involved with this language. This article has tried to explore the trend of research work on Bangla text summarization. Fourteen approaches have been briefly expounded here by addressing the pros and cons with some scope of improvement. A comparison has also been turned based on their incorporated features and evaluation results. It is expected that this article will draw the attention of more researchers in the area of Bangla text summarization and give a crystal-clear message about the opportunities to the next generation. The integrated message about all the existing methods has been depicted here to reveal the importance of Bangla text summarization. To the best of the author's knowledge, this is the first review study in this ground.
The Influence of Using Social Media as a Learning Platform by Bilingual and Multilingual Learners on English Speaking Skills

This study aims to discover the influences of using social media on the English speaking skills. To fulfill this study’s goal, data was collected using questionnaires, self-assessment, and speaking tests. Participants of the study were 20 bilinguals and multilingual of any two or more languages that are currently studying English as a foreign language (EFL) at the university level in Thailand. The result from speaking proficiency self-assessment shows the perception of bilinguals and multilingual who perceived that they can speak English fluently than accurately. The average score of the self-assessment is 5.59 out of 9. The standard deviation (S.D.) of their score is 1.46. This score shows that the participants consider themselves as competent users of English according to the description from British Councils (2012). Only eight out of 20 or 40 percent of the participants think that they have reached the level of capable users of the English, while the other 60 percent of participants think that they have not reached that level. From this study, it has been found that bilingual and multilingual speakers often use social media for educational purposes.
Activity recognition approach based on spatial-temporal constraints for aged-care in smart home

Activity recognition plays an important role in smart homes for aged-care. In this paper, we formulate the problem of activity recognition and propose a new method based on spatial-temporal constraints to carry out activity recognition, which consists of five phases: initialisation, segmentation, sensor data representation, activity exploration as well as activity identification. Besides, we analyse the time complexity and space complexity of our approach in theory. To evaluate our approach, we carried out experiments on real dataset from Wireless and Mobile Network Laboratory, Tamkang University. The experimental results demonstrate an improvement of 5.6% in the accuracy on average of recognised activities in comparison to the method of support vector machine (SVM).
Identifying Data Sharing and Reuse with Scholix: Potentials and Limitations

The Scholexplorer API, based on the Scholix (Scholarly Link eXchange) framework, aims to identify links between articles and supporting data. This quantitative case study demonstrates that the API vastly expanded the number of datasets previously known to be affiliated with University of Bath outputs, allowing improved monitoring of compliance with funder mandates by identifying peer-reviewed articles linked to at least one unique dataset. Availability of author names for research outputs increased from 2.4% to 89.2%, which enabled identification of ten articles reusing non-Bath-affiliated datasets published in external repositories in the first phase, giving valuable evidence of data reuse and impact for data producers. Of these, only three were formally cited in the references. Further enhancement of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies would be beneficial. The adoption of standardized data citations by journals will be critical to creating links in a more systematic manner.
Global Solar Radiation Characteristics at Calabar and Port Harcourt Cities in Nigeria

This study analyzed the inter-annual variability in solar radiation at Port Harcourt and Calabar, aiming at improving knowledge of solar resources. For the investigation, monthly mean global solar radiation data for fifteen years (2000 – 2014) was collected from Nigerian Meteorological Agency (NIMET), and the monthly mean extraterrestrial solar radiation was determined using globally recognized standard relation. The clearness index parameter was employed for characterizing the spatial variability of solar radiation for Calabar and Port Harcourt. The statistics of the monthly mean solar radiation deviations of Port Harcourt and Calabar was tested using the Kolmogorov–Smirnov method. The test results showed that they are normally distributed random variables. Furthermore, the analysis of sequential properties showed that the coefficients of the auto-correlation with lag 1 are significant for both stations. The auto-correlation coefficients with lag 1, though usually not significant, are negative for both stations. The auto regression lag 1 (AR-1) is the recommended procedure (model equation) for generating monthly solar radiation synthetic time series, with auto-correlation coefficients varying from 0.30 to 0.47 for both stations in the South-South of Nigeria. Citation: Amadi, S. (2020). Global Solar Radiation Characteristics at Calabar and Port Harcourt Cities in Nigeria. Trends in Renewable Energy, 6, 101-120. DOI: 10.17737/tre.2020.6.2.00114
Blockchain as a Platform for Secure Cloud Computing Services

Problems related to privacy and cyber-attacks have increased in recent years as a result of the rapid development of cloud computing. This work concerns secure cloud computing services on a blockchain platform, called cloud@blockchain, which benefit from the anonymity and immutability of blockchain. Two functions- anonymous file sharing and inspections to find illegally uploaded files- on cloud@blockchain are designed. On cloud@blockchain, cloud users can access data through smart contracts, and recognize all users within the application layer. The performance of three architectures- a pure blockchain, a hybrid blockchain with cache and a traditional database in accessing data is analyzed. The results reveal the superiority of the hybrid blockchain with the cache over the pure blockchain and the traditional database, which it outperforms by 500% and 53.19%, respectively.
Path Planning Algorithm to Enable Low Altitude Delivery Drones at the City Scale

The world of shipping business is expecting a transformation in the near future due to the rise of delivery via Unmanned Aerial Vehicles (UAV), commonly known as drones. However, the mass adoption of UAVs for delivery purposes faces in urban and suburban areas faces certain challenges that are unique and currently is an area of active research. For the reliable city-scale operation of delivery drones which are a swarm of autonomous Unmanned Aircraft Systems (UAS) operating at low altitude airspace outside line of sight, reliable algorithms to avoid obstacles, geofenced structures, and other drone traffic is absolutely necessary. This paper presents an improvement over state of the art on a path-planning algorithm that enables UAS to fly a designated mission factoring in geofencing and real-time traffic. This planning algorithm relies on a rapidly exploring random tree methodology to maintain clearance from other drone traffic and geofenced objects. Heuristic-based termination criteria for tree expansion allow for low computation times, which is a good fit for UAVs with limited computing capability onboard.