Reviewerly/exHarmony huggingface dataset:
An Improved MSCNN Method for Underwater Image Defogging

Underwater imagery is an important carrier and presentation of underwater information, which plays a vital role in the exploration, exploitation and utilization of marine resources. However, due to the limitations of objective imaging environment and equipment, the quality of underwater images is always poor, with degradation phenomena such as low contrast, blurred details and colour deviation, which seriously restrict the development of related fields. Therefore, how to enhance and recover degraded underwater images through post-production algorithms has received increasing attention from scholars. In recent years, with the rapid development of deep learning technology, great progress has been made in underwater image enhancement and restoration based on deep learning. In this paper, we propose an improved MSCNN underwater image defogging method, which combines Retinex and CLAHE for brightness equalization and contrast enhancement of underwater images, making the method more advantageous for complex situations such as low illumination, uneven illumination and obvious Rayleigh scattering phenomena in underwater environments, and conduct objective analysis and comparison of the recovered images to prove the effectiveness of this algorithm in underwater defogging and colour correction. The effectiveness of the algorithm for underwater defogging and colour correction is demonstrated by objective analysis and comparison of the recovered images.
An unsupervised generative adversarial network for single image deraining

As the basis of image processing, single image deraining has always been a significant and challenging issue. Due to the lack of real rainy images and corresponding clean images, most deraining networks are trained by synthetic datasets, which makes the output images unsatisfactory in real applications. Besides, note that a heavy rainfall is typically accompanied with some fog. Although some deraining networks have been proposed to remove the rain streaks in the rainy images, the output images may still be blurred due to the accompanied fog. In this paper, these problems existing in single image deraining is comprehensively considered, and propose a Cycle-Derain network based on an unsupervised attention-guided mechanism. Specifically, the Cycle-Derain network takes advantage of generative adversarial networks with two mappings and the cycle consistency loss to train both unpaired rainy images and rain-free images. Moreover, it introduces an unsupervised attention-guided mechanism and exploits the loop-search positioning algorithm to deal with the details of rain and fog in images. Extensive experiments have been carried out, and the results show that the proposed Cycle-Derain network is preferable compared with other deraining networks, especially in term of rainy image restoration.
Circuit Assemblies: Electronic Modules for Interactive 3D-Prints

This pictorial presents Circuit Assemblies, a design system for beginners to create 3D-printed interactive objects with embedded electronics. Circuit Assemblies are modules used to create objects that light up, move, or spin using basic electronic components like LEDs, batteries, and motors. To support beginners incorporating Circuit Assemblies into 3D designs, a set of virtual components were added to Tinkercad, a popular browser-based 3D CAD application with over 10 million users. In this paper, we begin with a set of design considerations gathered from interviews with three K-12 educators that teach electronics. We then present four different Circuit Assembly modules designed with these considerations in mind, highlighting the unique challenges that arise from combining electronics and 3D design for beginners, both in CAD software and physical assembly. We then present four different Circuit Assembly modules designed with these considerations in mind, highlighting the unique challenges that arise from combining electronics and 3D design for beginners, both in CAD software and physical assembly.
Image Retrieval Based on MPEG-7 Feature Selection Using Meta-heuristic Algorithms

The continuous growth of the internet and its diversified domains and tools; and the rapid development of new technologies have enormously increased the amount of digital images and consequently the dimension of the image data sets. Therefore, finding the relevant images to a query image in such database is a challenging task. Content-based image retrieval is the powerful method so far which use the visual characteristics of the image. The choice of these visual descriptors is a decisive phase in this area. This work presents a method which extracts the three image features which are: color, texture and shape based on the MPEG-7 standards. Then, the genetic algorithm is implemented for feature selection to provide the best extracted features to avoid unnecessary ones and reduce calculations and retrieval time. Meanwhile, the k-nearest neighbors algorithm is used to search for the relevant images. Finally, we will apply our method on two different image data-sets which contain images belonging to different domains to show the efficiency of selecting the correct features using meta-heuristic algorithms.
Mobile authentication scheme based on QKD and IBE

Abstract In order to handle the safety problems in mobile communication systems, a mobile identity authentication scheme based on QKD and IBE is proposed. First, the quantum key distribution technology is introduced to handle the safety problems between the private key generator and the cloud services. Next, the IBE algorithm is introduced to encrypt IMSI which makes the scheme more flexible and more efficient. Finally, the quantum time parameter is proposed to manage users’ secret key. Comparing with the existing schemes, the proposed scheme can make full use of the advantages of the QKD and IBE. The mobile identity authentication scheme based on QKD and IBE can be more safe and more efficient.
Workflow-based anomaly detection using machine learning on electronic health records’ logs: A Comparative Study

Timely access to patients’ healthcare records is very essential. As a result, broad access to EHR is mostly provided to users in efforts towards complying with the availability trait of the CIA. However, this opens up the system for abuse and misuse. This paper, therefore, analyzed the workflows of healthcare staff’s security practices in electronic health records (EHR) logs to determine anomalous security practices. Different classification types of machine learning algorithms were used. The EHR logs were simulated based on healthcare workflow scenarios. A number of machine learning algorithms were used to analyze the logs for deviations of accesses from the workflow. Based on the analysis results, all of the machine learning methods generally obtained a very good performance. The best performance on the non-normalized dataset is achieved by the Logistic Regression method with accuracy, precision, recall, and F1 value of 0.998, 0.849, 0.978, and0.909 respectively while Random Forest obtained the best result on Normalized data with accuracy, precision, recall, and F1 value of 0.998, 0.867, 0.836, and 0.851 respectively. It however remains challenging to detect malicious security practice if a malicious actor follows the workflow to access healthcare records with legitimate access right.
Privacy-Preserving PayString Service

PayString is an initiative to make payment identifiers global and human-readable, facilitating the exchange of payment information. However, the reference implementation lacks privacy and security features, making it possible for anyone to access the payment information as long as the PayString identifier is known. We propose an innovative solution, named PayStringSecure, for this issue by integrating a privacy layer based on Self-Sovereign Identity (SSI), Decentralized Identifier (DID) and Verifiable Credential (VC) to the PayString protocol. A working prototype has been developed to enrich the protocol with the new features.
Applications Research of Blockchain Technology in Accounting System

Abstract Accounting is the business language of social economy. The in-depth application of blockchain technology in the accounting industry can promote the development of the accounting industry itself, and also further promote the blockchain technology to play a higher role in the whole social economy. This paper studies the necessity and feasibility of the application of blockchain technology in the accounting industry, discusses the working principles of the accounting information system of the seller and the buyer, and constructs the vertical and horizontal application models of blockchain technology in the accounting system. Finally, this paper points out that we can strengthen the promotion of applications of blockchain technology in the accounting system by strengthening policy guidance, improving legislative protection, and cultivating compound talents.
User Behavior Analysis of a Mobile Short Video Application

Video accounts for a high proportion of Internet traffic and this proportion will continue to rise in the future. The mobile short video application represented by TikTok has a large user scale and is growing rapidly. An in-depth understanding of user behavior in mobile video systems can help us optimize video content distribution to reduce network pressure. In this paper, we have analyzed the user viewing data of mobile short videos and compare the results with traditional video analysis results. We found some characteristics of users' online mode, complete viewing, and likes. We also found that the popularity ranking and the like ranking of short video data conform to the Zipf-like formula. Finally, we did an experiment to prove that our data simulation system based on the Zipf-like formula can work well on short video data.
Parametric Audio-visual Quality Measurement Based on Interpretable Fuzzy Logic

In this paper, a fuzzy inference based system for the quality of experience assessment is presented. In particular, two models have been proposed to assess the perceived quality of experience. The first one is a global model which estimates the overall audiovisual quality without going through the individual evaluations of visual and auditory modalities while the other model has been created by merging fuzzy inference systems based on separate auditory and visual objective quality scores. Two different sets of parameters have been tested for the second model leading to two different measures. The contribution of this research lies in the application of the fuzzy inference logic to estimate the audiovisual quality of multimedia data. The experimental results on a publicly available quality dataset show competitive predictive performances of the proposed measures when compared to two existing audiovisual metrics based on random forests regression and multilayer perceptron.
Foreign Object Detection of Electric Transmission Line with Dynamic Federated Learning

Abstract The bird’s nests and other foreign objects on the transmission lines bring about huge threats to the electric power security. The aggregation of foreign objects data of different regions can boost the generalization of foreign objects with deep learning. However, due to policy constraints and other data security reasons, it is impossible to collect the data of different power companies to train a joint foreign object detection model. Therefore a dynamic federated learning based foreign object detection method for transmission lines is proposed. For the clients distributed in power companies, each model is trained and uploaded to the central server in an asynchronous way for dynamic fusion. For the model trained with pre-trained model, the pre-trained part is frozen and will not be trained. Thus the feature extraction backbone is not uploaded to central sever, which contributes to the decrease of the communication consumption by reducing the amount of uploaded data. The experimental results demonstrate that the dynamic federated learning based foreign object detection algorithm can maintain the same accuracy level compared with centralized training. It can train the joint model without uploading the original data of the edge nodes, which guarantees the security and privacy of local data. Compared with training individually, it has great improvement in accuracy. And it is also applicable with different network sizes. Therefore the dynamic federated learning based foreign object detection has strong privacy, accuracy, efficiency, flexibility and scalability.
The actual questions of the realization of the personality-oriented educational paradigm in the context of digitalization

In the context of digitalization, the existing educational paradigms are being transformed. Transformational processes are associated with the solution of essential problems at key nodes that are significant for the construction of the entire paradigmatic series. As such, the nodes in this study, the values, goals, and results of education, the content of education were considered, and the issues of implementation of the student-centered educational paradigm that was relevant for them in the context of digitalization were identified. The most important are the questions about the image of a person, which determines the direction of pedagogical efforts, about the place of eternal universal values rooted in human culture, among other value orientations of education, about the criteria for the selection of content and the representation in its structure of the personal experience of students and teachers, about didactically significant specifics of electronic media of content. The dialogue character of the educational content and interaction in the educational process, determined by the culture, is emphasized. A comparative analysis of knowledge-oriented, personality-oriented, and digital educational paradigms in the given key nodes has been carried out. The role of the teacher as a subject of a personality-oriented educational process, acting here in two guises: professional and personal, is noted. The problems and risks associated with the weakening of the personal approach in education are formulated.
HUMAN INVENTORSHIP IN EUROPEAN PATENT LAW

Abstract This article uses the advancements in artificial intelligence as the starting point for consideration of the role of human inventorship in European patent law. It argues that human inventorship is a necessary condition for the existence of an invention and inventive step, with the result that only products of human inventorship merit European patents. It identifies failings of European authorities to reflect this adequately in their approaches to determining patentability. Finally, it recommends recognising human inventorship as an implicit patentability requirement being an aspect of the statutory requirements for an invention and inventive step and extending applicant's disclosure duties correspondingly.
Team YahyaD11 at the Mowjaz Multi-Topic Labelling Task

This paper focuses of my enrollment in ICICS 2021 Competition Mowjaz Multi-Topic Labelling Task using Bidirectional Gated Recurrent Unit (Bi-GRU). The model is basically used to classify articles based on their topics that are present within its content. Mowjaz's topic are classified into ten categories and an article can be classified as under as many topics as it covers. In the evaluation, we regard the Mowjaz Multi-Topic labelling task as multi-classification task and use Unigram models to extract features to train a neural network classifier. In the result, the accuracy of my method reached 0.8232, ranking 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> .
A Novel Algorithm Using Content-Based Filtering Technology in Apache Spark for Big Data Analysis

Big data has been one of the fastest-growing research areas that many researchers are into, with large amounts of data being uploaded to the internet every minute analyzing this data can benefit businesses and content creators. Big companies are already making good use of customer data and can predict what customers might need and want based on their recent activities. In this paper, I am going to propose a novel algorithm that makes the process of analyzing big data much faster and easier by using content-based filtering on structured data in Apache Spark. We used models such as the classification model to classify the data in relevant categories that we need and find the relationship between days it takes to trend with views then use a predictive model to predict what type of content is good to produce. We used this on an existing realworld dataset and we were able to get good results. The results are encouraging and it proves that this is an improvement over traditional dig data analysis methods that uses unstructured data and deep learning for big data analysis when working on a very big dataset.
Impulse Noise Suppression through Variable Window Size in Color Images

In general, impulse noise is induced by bit errors in the course of image processing and transmission. Over the last decades, a growing range of updated median filters have been introduced but have their own shortcomings when detecting and restoring noise. In this article, we suggest weighted median filter approaches for salt and pepper (SAP) noise polluted color images using 3×3 and 9×9 window sizes. In our approaches, the pixel is either "noise free pixel" or "noise pixel" utilizing the detection stage. Directional variations and the amount of "right" pixels in the current filter window will adjust the observed noise pixels. The noise free pixel is retained by applying median filtering on it. Simulation results on the taken images show the proposed approaches as enhanced on the prevailing DAMF and BPDF de-noise methods with regard to PSNR, SSIM, NMSE and Computation Time with pictographic comparative study.
Design and Experimentation of Causal Relationship Discovery among Features of Healthcare Datasets

Causal relationships in a data play vital role in decision making. Identification of causal association in data is one of the important areas of research in data analytics. Simple correlations between data variables reveal the degree of linear relationship. Partial correlation explains the association between two variables within the control of other related variables. Partial association test explains the causality in data. In this paper a couple of causal relationship discovery strategies are proposed using the design of partial association tree that makes use of partial association test among variables. These decision trees are different from normal decision trees in terms of construction, scalability, interpretability and the ability of identifying causality in data. Normal Decision Trees are supervised machine learning approach to classify data based on a labeled attribute values. Variants of partial association trees are constructed as a part of analytics on a number of healthcare datasets. The applicability of design variants are carefully analyzed through this experimentation. In the above said experimentation it is found that the causation in data is not existed in data in some situations and sometimes the existing causality cannot be extracted where low associated dimensions are involved in data and hiding the underlying causality. One of the variants of proposed algorithms which was named dimensionality reduced partial association tree, did well in extracting causal association in case of a hidden causality in data.
Online teaching: a reflection

This article, written at the start of April 2021, is a personal reflection on what has and hasn't worked in remote/online education. I have drawn on my own experience of teaching over the course of the past year, observations of classroom practice I have undertaken as a mentor and middle leader with responsibility for teaching and learning in my school, and conversations I have had with colleagues in my school and elsewhere; it is, therefore, highly anecdotal, and the reader is asked to bear in mind the fact that, like many others, my journey into online teaching was enforced by the closure of schools during the first nationwide lockdown in March 2020. My core aim during both lockdowns was to provide for my students the best experience possible until such a time as we could all return to the physical classroom. As it became clear towards the end of 2020 and the start of 2021 that we were going to need to return to remote education, I began to think more deeply about the strategies I was employing in my online teaching, how effective they were for my students, and what I might do to maximise their learning experience and outcomes.
Backend Development using Spring JPA with PostgreSQL in Kotlin

Every web application needs to save and update data that is accessible via HTTP. This data must be secured. The backend, on the other hand, might be more challenging to get started with since seeing the effects of your work is more challenging. It's more command-line-centric, and it's also more reliant on jargon: everyone can figure out what a paragraph tag does, but GET and POST requests, though essential to backend development and the web, aren't as simple to grasp. We'll go over what back-end development is all about, as well as back-end programming methodologies, in detail in this post. Java with decades of history in professional enterprise development is great choice for any application secure stack. Kotlin similar to Java is used for implementation. Spring within Java ecosystem makes building resources secure. In this paper, we will be developing a backend service with Spring Data JPA to perform CRUD operations with PostgreSQL using Kotlin.
Exponent-Enhanced Attentive Knowledge Tracing based Online Learning Reinforcing

As an important component of online learning education systems, knowledge tracing (KT) has been the focus of numerous researches at present. However, most of the existing KT methods evaluate the relevance between exercises without paying attention to the association among exercises involving multiple concepts. A novel Exponent-Enhanced Attentive Knowledge Tracing (EEAKT) method is proposed to solve multi-concept problems, which integrates the attention mechanism and the conceptual layer of exercises. Based on the related analysis, the concept information through the nonlinear change of the exponential function will be identified when the concept is complex. The identified exercises are reinforced by attention mechanism. Extensive experiments on three real-world datasets show that EEAKT outperforms existing KT methods. Moreover, EEAKT can automatically identify the knowledge concepts that students need to update, which consolidate individual memory.
Hardware-oriented deep reinforcement learning for edge computing

A new deep reinforcement learning enhancement is proposed for edge computing. This work focuses on deep Q-networks (DQNs), which are used in deep reinforcement learning. Although DQNs are typically improved through a software-based approach, hardware-specific knowledge such as that on data paths and pipelines is used for improving a DQN. The DQN performance is improved and the number of resources are reduced through an efficient hardware design that considers the learning flow and parameter search. As the scale of the problem increases, the amount of reduction in the use of resources also increases. For example, when the size of the block catch game is 5 × 10, the memory requirement is reduced by approximately 50% compared to a previous DQN. The proposed hardware-oriented approach can be applied to any software technology. This study facilitates the development of novel technologies that can be realized through edge computing.