Reviewerly/exHarmony huggingface dataset:
Image blind restoration based on degradation representation network

Most deep learning (DL)-based image restoration methods have exploited excellent performance by learning a non-linear mapping function from low quality images to high quality images. However, two major problems restrict the development of the image restoration methods. First, most existing methods based on fixed degradation suffer from significant performance drop when facing the unknown degradation, because of the huge gap between the fixed degradation and the unknown degradation. Second, the unknown-degradation estimation may lead to restoration task failure due to uncertain estimation errors. To handle the unknown degradation in the real application, we introduce a degradation representation network for single image blind restoration (DRN). Different from the methods of estimating pixel space, we use an encoder network to learn abstract representations for estimating different degradation kernels in the representation space. Furthermore, a degradation perception module with flexible adaptability to different degradation kernels is used to restore more structural details. In our experiments, we compare our DRN with several state-of-the-art methods for two image restoration tasks, including image super-resolution (SR) and image denoising. Quantitative results show that our degradation representation network is accurate and efficient for single image restoration.
Procedural Information Security Countermeasure Awareness and Cybersecurity Protection Motivation in Enhancing Employee’s Cybersecurity Protective Behaviour

Radical changes in the Industrial Revolution 4.0 (IR4.0) have provoked several organizations in developed and emerging economies to constantly re-evaluate cybersecurity protective behaviour among their employees. Evidence from an emerging economy like Malaysia suggests that security awareness influences employees’ psychological factors and leads to enhancing employees’ cybersecurity protective behaviour to become actively engaged in effective cybersecurity practices. By overlooking these factors, employees are consequently exposed to various cybersecurity threats which could have adverse impacts on their organisations. Therefore, this study was conducted to explore the direct effect of procedural information security countermeasure (PCM) awareness on protection motivation components in enhancing employees’ cybersecurity protective behaviour. This study extended the Protection Motivation Theory (PMT) and the online survey was conducted to get feedback from the employees in various sectors within Klang Valley, Malaysia. A total of 245 responses were received. The results show that procedural security countermeasure awareness does positively influence protection motivation components, except for self-efficacy. Meanwhile, threat appraisal and coping appraisal positively influence some of the dimensions of cybersecurity protective behaviour. The research findings would serve as evidence that is fundamental to enhancing cybersecurity practices of urban community employees in Malaysia. Congruent with the diverse recent impacts of the IR4.0, the research would help to bolster cybersecurity-related policy implications that resonate with the Malaysian government agenda.
Property and data: A confused relationship

This chapter looks at how the relationship between property and data can cause legal confusion when the two interact in blockchain-based digital finance. Two types of crypto-assets, payment tokens and asset tokens are introduced and potential ways to give transactions legal certainty are suggested. Four court cases are used to demonstrate how a poorly-defined relationship between property and data can give rise to legal confusion, and how this can affect the court's approach to cryptocurrencies such as Bitcoin. An approach is proposed in which a distinction is made between property that consists of data, and data itself. This can help crypto-asset providers design internal data governance to protect both clients' property and their data. The extent to which this approach is in line with the provisions under the proposed EU MiCA and GDPR is discussed.
A method of Evaluation for Small and Medium-sized Enterprises

Abstract-Small and medium-sized enterprises (SMEs) have characteristics of small scale of development, poor anti-risk ability, and imperfect management. Timely and accurate evaluation of these enterprises is of great significance to corporate management, market supervision departments and social investors. Existing evaluation methods are mostly based on the internal financial information of mature enterprises, which are not suitable for small and medium-sized enterprises which have not yet achieved revenue capabilities, and have imperfect financial indicators. In this paper, we propose an evaluation model of the development trend of SMEs based on the enterprise knowledge graph. We obtain the information of these enterprises on the financial websites and then use entity recognition and relationship techniques to explore major events and relationships between enterprises, and build enterprise knowledge graph. We construct the feature sets of these enterprises, and use cluster analysis to classify and evaluate the enterprises. A graph-based neural network method is proposed to capture the deeper influence on the development trend among enterprises. The proposed method evaluates the development trend of SMEs from a new perspective.
Image retargeting quality assessment: A survey

With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm’s optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work.
A simple implement of Q-learning in robot path planning

Abstract This paper firstly gives an introduction of robot path planning problem, which includes the brief definition of path planning, some representative methods and previous applications of Q-learning. Secondly, the paper compares some typical methods, like Breadth First Search and Depth-First-Search, A* and deep learning, with corresponding pseudo codes in detail. Their advantages and disadvantages are also listed in this part. Thirdly, we carry out a simple simulation experiment by applying Q-learning method. The experiment is clearly presented in several parts which includes environment establishment, realizing the Q-learning, simulation experiment and interpretation of the Q-table. In the end, a short conclusion summarizes the achievement of our results.
Optimize the NOx emission concentration of Circulation Fluidized Bed Boiler based on on-line learning neural network and modified TLBO algorithm

The reduction of NOx emissions concentration of Circulation Fluidized Bed Boiler (CFBB) is an optimization problem, which can regarded as multi-inputs and single output problem. The first priority is to build NOx model. However, the combustion process of CFBB is complicated with strong coupling and nonlinear, etc. So a kind of on-line learning neural network is proposed to build online dynamic model of NOx emission concentration. Based on the established model, a modified teaching learning based optimization algorithm is used to optimize the boiler's parameters. Those parameters impact the NOx emissions concentration seriously. Experiment result shows that the NOx emissions concentration can be reduced by the two methods when the boiler runs with the optimizing operation data.
Bilateral and Median Filter Combination for High-Quality Cleaning of Random Impulse Noise in Images

The article proposes a method for cleaning impulse noise from random pixel intensity and location on images. The proposed method is based on a bilateral and median filter for impulse detection and noise removal. The experimental part showed that the proposed filter makes it possible to clean this type of noise with high efficiency. The method's effectiveness is especially noticeable at the high intensity of this type of impulse noise. The obtained results can be applied to various digital image processing tasks, such as medical image analysis and satellite image processing.
Development of a walking assistive robot for elderly people in outdoor environments

In this paper, we present a new robotic system to assist elderly people during walking in outdoor environments. The walking assistive robot is equipped with depth camera, GPS, compass and ultrasonic sensors. The robot generates the route to the goal location using the google map. Based on the sensor information the robot follows the generated route while avoiding obstacles. In addition, the robot adjusts its speed based on the walking speed of the user and sends information to the family members of the user about the location in the google map. The robot checks the condition of the user and his/her distance to the robot and takes the appropriate decisions. The usefulness of the proposed system is examined experimentally.
SuperDetector: A Framework for Performance Detection on Vulnerabilities of Smart Contracts

Abstract The technology of Ethereum blockchain enables the implementation of smart contracts. Nowadays smart contracts are one of the most successful applications of blockchain technology, which are widely used in many fields, such as finance, energy and services. The decentralization and immutability properties of Ethereum blockchain provide security for transactions from smart contracts. However, these properties are possible to lead to unfixable vulnerabilities of smart contracts. In recent years, vulnerability detection on smart contracts has attracted more attention from researchers and many related tools have emerged. Nevertheless, the existing vulnerability detection tools have not yet been put into formal use. Due to the lack of suitable smart contract sets and fair metrics with other factors, it remains complex work to conduct authoritative performance tests on these tools. In this paper, we first summarize eight common vulnerabilities of smart contracts, and then divide them into call-related and call-irrelated vulnerabilities according to whether they involve caller functions or not. In addition, we propose a detection framework called SuperDetector, which combines a variety of vulnerability detection tools based on static analysis technology. And the framework mainly measures the detection performance, which contains vulnerability coverage, detection accuracy, usability and execution time on the two types of vulnerabilities. The results indicate that the framework can analyze the performance from different perspectives, and the performance of vulnerability coverage and detection accuracy on call-related vulnerabilities is much better than that on call-irrelated vulnerabilities. Finally, we design a selector based on the detection results in the framework for improving the vulnerability coverage and detection accuracy.
Text Clustering of COVID-19 Vaccine Tweets

The advent of the novel coronavirus disease (COVID-19) in late December 2019 led to the dramatic loss of human life worldwide and presented an unprecedented challenge to public health, education, social life, world economics, and the world of work. Equal access to safe and effective vaccines is very vital to ending the coronavirus pandemic. This research paper aims to perform text clustering on COVID-19 vaccine tweets. It investigates the optimal number of clusters prevalent in the COVID-19 vaccine corpus using deep learning techniques and machine learning algorithms. The study also investigates how using word embeddings can improve the accuracy of the proposed models by evaluating unsupervised learning methods. Machine learning clustering algorithms such as k-means and HDBSCAN, deep learning-based clustering techniques, and UMAP a dimensionality reduction algorithm were employed to perform text clustering. The results of this research showed the optimal clusters obtained by using deep learning clustering techniques and machine-learning algorithms for text clustering. HDBSCAN clustering algorithm showed better clustering results based on features learned while k-means performed better clustering based on various evaluation metrics.
Locate, Execute, Expect Design Pattern

This article is about Locate Execute Expect design pattern. In the beginning we will discuss about the purpose of this pattern.We will review the general structure of a classic framework where there is a Page Object Model structure and then we will present the Locate Execute Expect structure. We will show some advantages and disadvantages for both patterns.After this comparison, we need to see the differences in a real-world scenario, and we will show you a general structure of an automation framework that does not implement a design pattern. After that we will show how the structure looks like when we have the Page Object Model and Locate Execute Expect pattern implemented.We will try to apply Locate Execute Expect pattern to a project and see how easy is to achieve this. This pattern is very flexible, and it can be used also in combination with Gherkin which is one of the most used behavior-driven development languages.In the end, we will learn when we should use this pattern and when we should avoid it.
Wrapped phase denoising using adaptive Kalman smoother algorithm

A wrapped phase pattern denoising algorithm is proposed based on the adaptive Kalman filtering. The exponential phase field (EPF) corresponding to the noisy wrapped phase pattern is considered as the measurement data. Row-wise and column-wise phase denoising is performed in a sequential manner. In each row/column, the EPF is denoised using the Kalman filter considering it as a spatially varying auto-regressive process. For achieving measurement independent, automatic phase denoising, the system and measurement noise covariances are adaptively estimated in the implementation of Kalman filter. The proposed formulation also allows simultaneous denoising of multiple rows/columns to reduce the computation cost. The amplitude of denoised EPF can be used as a phase quality metric which can aid in the subsequent phase unwrapping operation. The denoising performance is quantitatively evaluated using simulation results. Experimental results are provided to further substantiate the practical applicability of the proposed method.
Combining Deep Learning and the Source-Scanning Algorithm for Improved Seismic Monitoring

ABSTRACT In this study, we develop an integrated framework for simultaneous detection of seismic events and picking phase arrival times, phase association, and locating earthquakes. The proposed model combines the accuracy of convolutional neural networks for classification tasks and the efficiency of waveform-based algorithms for identifying coherent seismic arrivals. We find that our model strongly dominates the classic techniques, especially in identifying small magnitude earthquakes. We apply our model to one month of continuous seismic data recorded in western Canada for monitoring seismic activity associated with fluid injection operations. In comparison with previously developed deep-learning models, our technique reveals a nearly identical performance without human interaction during the entire process of picking the phase arrival times and locating the associated events.
Catastrophe control of aphid populations model

Considering the effect of the natural enemy on aphid populations, the corresponding model with delay is built. The model is analyzed using the qualitative theory of differential equations and catastrophe theory etc. For the outbreak phenomenon of aphid populations, the corresponding management model is proposed and the catastrophe controller is designed to keep the system in a virtuous cycle by means of the qualitative theory of impulsive differential equations. In the mean time, some simulations are carried to prove the results. The paper not only provides a new method for catastrophe control but also expands the application fields of catastrophe control.
Personal e-Learning Environment of the Maths teacher’ online course as a means of improving ICT competency of a Mathematics teacher

Abstract The paper studies the issue of increasing the ICT competency of a Maths teacher. The study analyzes the concept of ICT competency of a Maths teacher and considers the possibilities of its formation in different countries. One of the ways to increase the ICT competency of a Maths teacher is to use the “Personal e-learning environment of the Maths teacher” online course. The authors describe the model of the developed course, which is based on the ADDIE model and takes into consideration the key activities of a teacher. The experiment, conducted among master’s students majoring in “Secondary education. Mathematics” showed the positive effects of the developed course on the level of their ICT competency. The redistribution of the percentage of the students towards normative and high levels of ICT competency formation was observed.
Augmented and Virtual Reality-Driven Interventions for Healthy Behavior Change: A Systematic Review

Augmented Reality (AR) and Virtual Reality (VR) have shown potential benefits in managing healthy behavior. This paper presents a systematic review of AR- or VR-driven interventions for promoting healthy behaviors. The review investigates the design, implementations of the intervention, persuasive strategies, intervention platforms, underlying technologies, current trends, and research gaps. Our review of the past 10-years' work in the area reveals that 1) the considered papers focused on seven main healthy behaviors, where “alcohol use” emerged as the most commonly considered behavior; 2) trustworthiness emerged as the most commonly used persuasive strategy; 3) youth are the most targeted audience; 4) VR is more common than AR; and 5) most AR- or VR-driven interventions are perceived to be effective in motivating healthy behavior in people. We also uncover how they use Artificial Intelligence and Object Tracking in this space. Finally, we identify gaps and offer recommendations for advancing research in this area.
PACk

The Agglomerative Hierarchical Clustering (AHC) algorithm is widely used in real-world applications. As data volumes continue to grow, efficient scale-out techniques for AHC are becoming increasingly important. In this paper, we propose a Partition-based distributed Agglomerative Hierarchical Clustering (PACk) algorithm using novel distance-based partitioning and distance-aware merging techniques. We have developed an efficient implementation of PACk on Spark. Compared to the state-of-the-art distributed AHC algorithm, PACk achieves 2X to 19X (median=9X) speedup across a variety of synthetic and real-world datasets.
Sunflower: An Interactive Artistic Environment based on IoMusT Concepts

The Internet of Musical Things (IoMusT) is an interdisciplinary area that aims to improve the relationship between musicians and their peers, as well as between musicians and audience members, creating new forms of interaction in concerts, studio productions, and music learning. Although emerging, this field already faces some challenges, such as lack of privacy and security, and mainly, lack of standardization and interoperability between its devices. Therefore, an environment design, called Sunflower, was proposed, which tries to contribute to solving the most recurrent problems in this area, specifying an architecture pattern, protocol, and sound features that aim to allow heterogeneity in these systems. Its practical implementation resulted in an interoperable, multimedia, and interactive environment. This paper, therefore, shows a demonstration of how Sunflower works in the accomplishment of an artistic presentation, also emphasizing its approach, the technologies that support it, and the advances it can bring to the area of IoMusT.
Knowledge Sharing in International Business

In an era where global trade has developed along with the massive number and expansion of knowledge, the necessity of exchanging knowledge and international business has continuously increased. This is in light of international business organizations’ intellectual capital, which propels them into global competition and provides them with a competitive advantage if utilized properly. The purpose of this research is to examine and assess two topics, namely, knowledge sharing and international business, as well as their location in research studies, importance, definitions and histories of those terms, and research interests for each one of these two dimensions. The review focused on the topic of knowledge sharing in international business organizations, with analysis and discussion of the most important research, studies, and trends along three axes: knowledge sharing, international business organizations, and earlier studies. The study concluded that knowledge sharing is an essential aspect of knowledge management since it connects the previous processes of acquiring knowledge capital to the subsequent processes of application and practice, which is especially important for international business organizations that need to benefit from knowledge in a variety of ways. In addition to the scarcity of studies that coupled knowledge sharing and international business, there is a great scope for developing models and applications in this field of study. We were unable to find studies that combined the two themes through research, but they were implicitly addressed within the research to highlight the importance of knowledge sharing in international business, and this is what motivates more research in this field due to the shortcoming of studies on the subject. It seemed to be that the essential element to organizational innovation is knowledge development and sharing.
Symmetry Weight-sharing for Patch-based Stereo Matching

Abstract Neural networks are becoming more popular than traditional methods in stereo matching. The networks can be decomposed into four sub-modules: feature extraction / matching cost computation, cost aggregation, disparity computation / optimization, and disparity refinement. A typical design for the feature extraction networks is that the left and right branches share the same weights. However, the Siamese networks are weak at distinguishing neighboring patches because of the interference of geometric distortion on slanted surfaces. This paper proposes symmetry weight-sharing to improve the feature extraction networks. The geometry of feature extraction and patch comparison has been analyzed, which shows that symmetry weight-sharing can fulfill the geometry on slanted surfaces. A half-translation module is proposed to implement symmetry weight-sharing without additional computational costs. Experiments on the KITTI 2012 and KITTI 2015 datasets show that the symmetry weight-sharing networks have better performance than the weight-sharing networks.