Reviewerly/exHarmony huggingface dataset:
A Novel Starlight-RGB Colorization Method Based on Image Pair Generation for Autonomous Driving

It is a difficult challenge for humans to carry out environmental perception work at night and in low-light scenes. Depending on its extraordinary working performance in the dark, starlight camera is widely used in night driving assistance and various surveillance missions. However, the starlight camera images are lack of colorful information, which prevents users from understanding. This paper proposes a novel approach for colorizing starlight images using Generative Adversarial Network (GAN) architecture. The proposed method overcomes the time-space asynchronism of traditional heterogeneous data acquisition. We firstly introduce starlight-RGB image pairs generation. Inspired by 3D perspective transformation, we use LiDAR, camera and Inertial Measurement Unit (IMU) data to create generated visible images. We collect synchronous visible images, LiDAR points data and IMU data in the daytime and acquire LiDAR, starcam and IMU data at night. Such image pair generation method overcomes the difficulty of obtaining pairs of data and image pairs are aligned at pixel-level. As there are no reflection LiDAR points in the sky, the perspective projection images have no content in the sky areas. Based on supervised image-to-image translation GAN architecture, we use daytime RGB images as unpaired data, which is in order to restore the texture and color of the sky. We use KITTI dataset as validation, and get good experimental performance on our datasets.
Universal and General Quantum Simultaneous Secret Distribution with Dense Coding by Using One-Dimensional High-Level Cluster States

A universal and general quantum simultaneous secret distribution (QSSD) protocol is put forward based on the properties of the one-dimensional high-level cluster states, in which one sender dispatches different high-level classical secret messages to many users at the same time. Due to the idea of quantum dense coding, the sender can send different two-dit classical messages (two d-level classical numbers) to different receivers simultaneously by using a one-dimensional d-level cluster state, which means that the information capacity is up to the maximal. To estimate the security of quantum channels, a new eavesdropping check strategy is put forward. Meanwhile, a new attack model, the general individual attack is proposed and analyzed. It is shown that the new eavesdropping check strategy can effectively prevent the traditional attacks including the general individual attack. In addition, multiparty quantum secret report (MQSR, the same as quantum simultaneous secret submission (QSSS)) in which different users submit their different messages to one user simultaneously can be gotten if the QSSD protocol is changed a little.
Prototype Reinforcement for Few-Shot Learning

Few-shot learning requires to recognize novel classes with scarce labeled data. The effectiveness of Prototypical Networks has been recognized in existing studies, however, training on the narrow-size distribution of scarce data usually tends to get biased prototypes. In this paper, we figure out two influencing factors of the process: the feature redundancy and the feature monotony. We propose a simple but effective method to reinforce the prototype. In our method, feature transformation and feature drift are used to reduce the feature redundancy of the prototype. Besides, the pseudo-label strategy is used to recalculate the prototype, enrich the features of the prototype, and alleviate the problem of feature monotony. Effectiveness is shown on two few-shot benchmarks, mini- ImageNet, and CUB. The experimental results show that compared with the ordinary mean prototype, the reinforcement prototype can effectively improve the classification accuracy.
How to Solve the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is believed to be an intractable problem and have no practically efficient algorithm to solve it. The intrinsic difficulty of the TSP is associated with the combinatorial explosion of potential solutions in the solution space. When a TSP instance is large, the number of possible solutions in the solution space is so large as to forbid an exhaustive search for the optimal solutions. The seemingly “limitless” increase of computational power will not resolve its genuine intractability. Do we need to explore all the possibilities in the solution space to find the optimal solutions? This chapter offers a novel perspective trying to overcome the combinatorial complexity of the TSP. When we design an algorithm to solve an optimization problem, we usually ask the critical question: “How can we find all exact optimal solutions and how do we know that they are optimal in the solution space?” This chapter introduces the Attractor-Based Search System (ABSS) that is specifically designed for the TSP. This chapter explains how the ABSS answer this critical question. The computing complexity of the ABSS is also discussed.
Proposing Innovative Perturbation Algorithm for Securing Portable Data on Cloud Servers

Cloud computing provides an open architecture and resource sharing computing platform with pay-per-use model. It is now a popular computing platform and most of the new internet based computing services are on this innovation supported environment. We consider it as innovation supported because developers are more focused here on the service design, rather on arranging the infrastructure, network, management of the resources, etc. These all things are available in cloud computing on hired basis. Now, a big question arises here is the security of data or privacy of data because the service provider is already using the infrastructure, network, storage, processors, and other more resources from the third party. So, the security or privacy of the portable user's data is the main motivation for writing this research paper. In this paper, we are proposing an innovative perturbation algorithm MAP() to secure the portable user's data on the cloud server.
An Exploratory Study of Copyright Inconsistency in the Linux Kernel

Software copyright claims an exclusive right for the software copyright owner to determine whether and under what conditions others can modify, reuse, or redistribute this software. For Free and Open Source Software (FOSS), it is very important to identify the copyright owner who can control those activities with license compliance. Copyright notice is a few sentences mostly placed in the header part of a source file as a comment or in a license document in a FOSS project, and it is an important clue to establish the ownership of a FOSS project. Repositories of FOSS projects contain rich and varied information on the development including the source code contributors who are also an important clue to establish the ownership. In this paper, as a first step of understanding copyright owner, we will explore the situation of the software copyright in the Linux kernel, a typical example of FOSS, by analyzing and comparing two kinds of datasets, copyright notices in source files and source code contributors in the software repositories. The discrepancy between two kinds of analysis results is defined as copyright inconsistency. The analysis result has indicated that copyright inconsistencies are prevalent in the Linux kernel. We have also found that code reuse, affiliation change, refactoring, support function, and others' contributions potentially have impacts on the occurrence of the copyright inconsistencies in the Linux kernel. This study exposes the difficulty in managing software copyright in FOSS, highlighting the usefulness of future work to address software copyright problems.
GAMSNet: Deblurring via Generative Adversarial and Multi-Scale Networks

Traditional deblurring algorithms such as blur kernel estimation and gradient prior methods, these methods can only deal with a single type of blurry image, and such methods are very time-consuming. Due to the excellent performance of deep learning neural networks in the field of image generation, this paper regards the traditional image deblurring problem as a special image transformation problem. First, a multi-scale network is used to restore the blurry image, the network can restore the image in an end-to-end manner. Then, the multi-scale network will output a restored image. Finally, the generative adversarial network (GAN) can make the restored image closer to the real clear image in the sense. In terms of loss function, perceptual loss in content loss is adopted to ensure the consistency of the generated image and clear image in content. The results of deblurring will be evaluated by testing the peak signal to noise ratio (PSNR) and structural similarity (SSIM) between the generated image and the clear image. The results show that the PSNR and SSIM of the algorithm in the GoPro dataset have reached 28.01 and 0.8538, respectively, and can recover some details lost due to shake blur.
Convolutional neural network libraries benchmarking

Context: The growing use of Convolutional Neural Networks (CNNs) in scientific experiments and industry has resulted in the development of several Convolutional Neural Networks libraries. There is a need on the part of developers and scientists to identify scholarly works that evaluate the performance of these libraries on different computer architectures. Objective: Identify and systematize the CNN libraries benchmarkings. Method: A systematic literature mapping was conducted to analyze the scientific research in the field. Results: Systematic mapping found 12 papers that evaluate the performance of Convolutional Neural Networks on different computer architectures. Conclusion: The state of the art has been mapped, there are still few works that evaluate Convolutional Neural Networks libraries with statistical accuracy.
An Improved Random Forest Model Combined with Bootstrap and Under sampling for Urban Management Case Classiftcation

Digital grid management is a promising direction of urban management, while the current classification of urban management cases in the digital urban management system is mainly based on the subjective judgment, which might result in inefficiency and even errors. In classification, the random forest (RF) is an efficient and powerful algorithm, yet it performs less satisfactorily due to its traditional Bootstrap resampling when faced with a large amount of imbalanced data. In this study, we propose an improved random forest model combined with Bootstrap and Under sampling (RFCBU) to address this problem. In RFCBU, we employ both Bootstrap and Under sampling to train some of the classification and regression trees (CART). To evaluate its validity, we compare it with 4 basic models, namely logistic regression (LR), gradient boost decision tree (GBDT), extreme gradient boosting (XGB), and the original random forest (RF), with the data from Shenzhen digital management system. Experimental results verify the validity of RFCBU in terms of accuracy and receiver operating curve (ROC).
Study of Sign Language Recognition Using Wearable Sensors

Sign language was designed to allow hearing-impaired person to interact with others. Nonetheless, sign language was not a common practice in the society which produce difficulty in communication with hearing-impaired community. The general existing studies of sign language recognition applied computer vision approach; but the approach was limited by the visual angle and greatly affected by the background lightning. In addition, computer vision involved machine learning (ML) that required collaboration work from team of expertise, along with utilization of high expense hardware. Thus, this study aimed to develop a smart wearable American Sign Language (ASL) interpretation model using deep learning method. The proposed model applied sensor fusion to integrate features from six inertial measurement units (IMUs). Five IMUs were attached on top of the each fingertip whereas an IMU was placed on the back of the hand’s palm. The study revealed that ASL gestures recognition with derived features including angular rate, acceleration and orientation achieved mean true sign recognition rate of 99.81%. Conclusively, the proposed smart wearable ASL interpretation model was targeted to assist hearing-impaired person to communicate with society in most convenient way possible.
Modeling of selected set of software requirements using UML class diagrams

Unified Modeling Language (UML) is used to model and design the software requirements. Among various components of UML, class diagram is employed to model the software requirements in terms of objects organized in classes and relationships between them. In real life applications, it has been observed that a system may have several requirements and it is not possible to model each and every requirement because of time, budget, and other constraints of an organization. Based on our review, we found that, in literature less attention is given to the modeling of the software requirements from the selected set of the requirements using UML class diagrams. Therefore, to address this issue, in this paper we present a method for modeling of the selected set of the software requirements using UML class diagrams. The applicability of the proposed method is discussed with the help of the software requirements of an institute examination system.
The Embedded Strategy of Enterprise Innovation Network Based on Knowledge Base

Constructing the innovation network is an important means of breakthrough in the traditional manufacturers for the emerging e-commerce platform, based on two different levels of innovation network node, content and structure, YEATION and YOUPIN were chosen to analyse and research the relationship and between knowledge base and the embedding strategy of electric business platform enterprises to create innovation network. To put forward the knowledge base width wider, insufficient depth of the enterprise should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. At the same time, it is proposed that platform enterprises should not adopt a single embeddedness strategy, but should adopt auxiliary embeddedness strategy in a timely manner.
A case study by using Python to implement data and dimensionality reduction

The purpose of this study is to research and explore the Data Dimension, and propose the data feature & selection of dimensionality reduction technique, in order to help users understand the impact and meaning between dimensionality reduction parameters and data dimension, thereby strengthening the use of dimension reduction algorithm. In previous studies, many scholars have proposed dimensionality reduction algorithms for various data types, such as Multi-Dimensional Scaling (MDS), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Facet Analysis (FA), Isometric Feature Maps (Isomap, using for manifold analysis), Local Linear Embedding (LLE), and Laplacian feature maps (Laplacian Eigenmaps). Most of these algorithms do not need to set parameters, and it has been obtained during the experiment that the selection of parameters has no visual analysis effect on the dataset in this experiment, and should be determined according to the feature of the dataset. This study is conducted by comparing the most used PCA and LDA dimensionality reduction techniques, as well as the analysis of merging other similarity methods while using MDS to process mixed data.
Towards Explainable Artificial Intelligence and Explanation User Interfaces to Open the ‘Black Box’ of Automated ECG Interpretation

This an exploratory paper that discusses the use of artificial intelligence (AI) in ECG interpretation and opportunities for improving the explainability of the AI (XAI) when reading 12-lead ECGs. To develop AI systems, many principles (human rights, well-being, data agency, effectiveness, transparency, accountability, awareness of misuse and competence) must be considered to ensure that the AI is trustworthy and applicable. The current computerised ECG interpretation algorithms can detect different types of heart diseases. However, there are some challenges and shortcomings that need to be addressed, such as the explainability issue and the interaction between the human and the AI for clinical decision making. These challenges create opportunities to develop a trustworthy XAI for automated ECG interpretation with a high performance and a high confidence level. This study reports a proposed XAI interface design in automatic ECG interpretation based on suggestions from previous studies and based on standard guidelines that were developed by the human computer interaction (HCI) community. New XAI interfaces should be developed in the future that facilitate more transparency of the decision logic of the algorithm which may allow users to calibrate their trust and use of the AI system.
A proposed OKR-based framework for cyber effective services in the GDPR era

This chapter will focus on the following areas which are GDPR, data breaches, digital identity theft, and digital identity schemes. The chapter will explore each subject in detail and then examine the correlation between the four whereby cybersecurity is deemed to be the common factor. Whilst there have been numerous studies on each subject in silo, this study presents the first unique opportunity to correlate the four post GDPR. Furthermore the study sets out to understand the effectiveness of cybersecurity controls of organisations that provision services in this era, whereby a survey has correlated professionals’ attitudes to GDPR compliance and good cybersecurity practice. The study aims to influence investment decisions of executive boards with regard to their investment spend on cyber defences through the introduction of an OKR framework to effect cyber effective defences in the GDPR era.
Applying Elements of Smart Educational Environments in Business Decision Support Centers

In modern conditions, requirements for the level of training of analysts, the need for training experts for modern decision support centers for managing business systems (situational centers) requires the use of modern educational technologies that can use the information accumulated in the systems, experience analysis and problem-solving, external sources for the formation of skills and abilities to analyze in new or complex crisis situations and the preparation of appropriate management decisions.
Eyes speak out Mind: Deep models for Gaze-based Analysis of Bilingual And Monolingual Reading

This paper presents an approach that attempts to explore effects of bilingualism in reading by analysis of eye-gaze data. As readers are more comfortable in reading first language (L1) compared to second language (L2), their eyes move differently in these two cases. Being motivated by this hypothesis, a deep learning model is developed to predict whether a reader is reading first or second language by analyzing the reading behavior given by the eye-tracking data. Exposure to different languages simultaneously may influence the proficiency in native language reading and this matter is also investigated by using deep learning model which can differentiate monolingual readers from bilingual readers analyzing their eye movement during reading in their respective native language. Experiments are conducted on GECO Corpus which provides eye-tracking data of 19 readers reading a novel in L1 and L2 along with eye-tracking data of several monolingual readers reading same text in their native language. Experimental results show that the proposed models are quite efficient in capturing the differences in eye gaze pattern for reading L1 and L2 for a reader as well as in identifying monolingual and bilingual readers by processing eye-gaze data.
Rectangle Template Matching Based on Gradient-Chromatic Illumination-Invariant Images

Template matching is widely used in computer vision and pattern recognition for its efficiency. However, conventional matching algorithms fail to match same templates of a same scene when illumination changes, especially when shadows exist. To achieve higher accuracy of template matching under different illuminations, a template matching algorithm is proposed based on gradient-chromatic illumination-invariant image. The matching results on Middlebury 2014 stereo dataset shows that our algorithm is illumination-invariant and achieve higher accuracy of rectangle template matching.
Towards Innovative Education: Developing Digital Learning Strategy in a Modern University

In consequence of technological advances escorted by social changes, higher education stands now on the threshold of substantial renovation. New individualized and personalized pedagogical approaches, which are accompanied by the development of novel modes and methods of educational provision, are aimed at enhancing the involvement of people of different ages and backgrounds in higher education by creating more flexible learning opportunities associated with digital transformation of education. Modern universities have to create long-term institutional strategies for high-quality education and research with due account for recent developments in the global higher education market, including the so-called “digital strategies” designed in accordance with the goals and objectives set in a university’s strategic plan. Digital strategies embrace a set of institutional methodologies, practices and tools for delivering a digitally enriched curriculum. This paper aims at exploring the novel approaches, innovative educational modes and methods utilized by a university in course of implementing a digital strategy developed for creating a supportive digital learning environment that would stimulate the improvement of transversal skills necessary for the contemporary knowledge-based economy. The following objectives were formulated by the author: 1) to study recent developments in the higher education area in the context of its modernisation; 2) to examine digital learning strategies that can be developed by a university for building a supportive digital learning environment; 3) to describe novel educational modes applied by a university in the frame of implementing a digital learning strategy in the agenda of digital transformation of higher education. The following research methods were utilized by the author: Review of the EU official documents dedicated to recent developments in European higher education. Analysis of digital learning strategies of randomly chosen universities aimed at building a supportive digital learning environment. Research implications suggest how the research findings may be used by the management for designing an effective digital strategy in the framework of a comprehensive institutional strategy aimed at reaching academic and research excellence.
Application Problems and Development Modes of End-to-End Intelligent Speech Model

The end-to-end intelligent speech model is based on deep neural network to directly predict the probability of speech signal and target language character from the raw data. The intermediate processing and neural network are integrated, which greatly simplifies the task processing steps. However, due to the complexity of the internal logic of the model, it cannot effectively balance and take into account the advantages of a single intelligent speech technology. In the future, the application of end-to-end integrated model will have further development in the field of speech. The one is “front-end to back-end” end-to-end model, which integrates speech enhancement and acoustic modeling. The other one is “speech-to-speech” end-to-end model, it focuses on extracting and processing information of speech signal, making the man-machine interaction closer to the real human language communication.