Reviewerly/exHarmony huggingface dataset:
Practice and Exploration of Innovation and Entrepreneurship Education for Computer Science and Technology Professionals Based on the Analysis of Big Data

Abstract According to the analysis of big data, in the process of computer science and technology talent management, it is necessary to take effective technology innovation and development as the core and pay attention to the expansion and promotion of the industry. According to the existing needs of comprehensive computer technology, it is necessary to train professional computer technology talents and strengthen the expansion of the technical model under the professional technology, combined with the existing technical development needs. Moreover, we should constantly integrate the control market of computer science talents, pay attention to the training and improvement of technology and carry out effective implementation of basic knowledge. According to the existing standard mode, we will have to adjust the development demand of specialization to ensure the professional technology integration under the innovation and development of computer specialty.
Subband Adaptive Enhancement Of Low Light Images Using Wavelet-Based Convolutional Neural Networks

Images captured in low light condition have a narrow dynamic range with a dark tone, which are seriously degraded by noise due to the low signal-to-noise ratio (SNR). Discrete wavelet transform (DWT) is invertible and thus is able to decompose an image into subbands without information loss minimizing redundancy. In this paper, we propose subband adaptive enhancement of low light images using wavelet-based convolutional neural networks. We adopt DWT to achieve joint contrast enhancement and noise reduction. We combine DWT with convolutional neural networks (CNNs), i.e. wavelet-based CNN, to facilitate subband adaptive processing. First, we decompose the input image into LL, LH, HL, and HH subbands to get low and high frequency components. Second, we perform contrast enhancement for LL subband and noise reduction for LH, HL and HH subbands. Finally, we perform refinement to enhance image details. Experimental results show that the proposed method enhances low light images while successfully removing noise as well as outperforms state-of-the-art methods in terms of visual quality and quantitative measurements.
Research on Temperature Prediction of Spacecraft Cable Based on BP Neural Network

The cable is a key component for spacecraft to transmit energy, power and signal. Because of its resistance, it will inevitably generate heat under the action of a long time current. Therefore, temperature is the main factor affecting the life of spacecraft cable. This paper proposes an intelligent algorithm model based on Back-Propagation (BP) neural network. The algorithm, which does not depend on the complex characteristic parameters of the spacecraft cable itself, realizes the temperature calculation through training the BP network. The simulation results show that the algorithm has the advantages of high accuracy and fast convergence speed. The calculated temperature can guide and improve the design of cable layout and direction. Further calculated cable life can provide a strong foundation for future spacecraft to distribute electrical energy intelligently and autonomously.
Slap swarm algorithm with memory mechanism and boundary collision processing

Abstract Aiming at the problem of that the standard salp swarm algorithm has low result precision and slow convergence velocity in the evolutionary process, an improved salp swarm algorithm optimization based on memory mechanism and boundary collision processing is proposed. Firstly, a Chebyshev chaotic map was used to initialize the salps to make them distribute more evenly in search space. Secondly, adding the memory mechanism introduces the individual history optimum of salps into the optimizing strategy of individual salps, which accelerates the convergence speed of the algorithm. Finally, the boundary collision rebound mechanism is introduced to ensure the effectiveness and diversity of the population. The improved algorithm is simulated on 12 types of benchmark test functions in this paper, and compared with other intelligent optimization algorithms under the same conditions. The results show that the improved algorithm has not only a obvious improvement in convergence speed and convergence accuracy, but also has better optimization performance.
Imbalanced Dataset Visual Recognition by Inductive Transfer Learning

Computerized picture is a blend of picture components called pixels, each with a discrete and finite numeric value for its intensity level. Image classification is the process of extracting this pixel features in a digital image and based on this features the image is assigned into a class category. Transfer learning has been quite successful in processing image classification task with less number of training images. Motivated by the immense application of transfer learning methodology, we propose a novel architecture for classifying fine-grained and low resolution images with inter-class and intra-class similarity. In the proposed model we are examining the accuracy level of knowledge transfer between source and target domains having heterogeneous and homogeneous feature spaces and label spaces. The datasets we are using are Oxford 102 flower dataset and Caltech 101 dataset. The Inductive transfer learning methodology is being adapted. As the available target dataset per class is less, we are emphasizing few-shot learning. Supervised learning method is used for feature construction. ResNet50 deep neural networks are used as fixed feature extractors. The accuracy exhibited by proposed architecture is compared with the state-of-the-art image classification models.
Research on Non-stationary Acoustic Signal Filtering Method Based on Wavelet Threshold Denoising

In order to deal with the noise problem in the process of acquiring sound information, wavelet analysis method is used in this paper: (1) The traditional wavelet analysis method has the problems of "overkill" and large deviation of the reconstructed signal. By analyzing the wavelet decomposition process and fitting the change of the noise with the decomposition layer, a dynamic threshold calculation method is proposed, which optimized the signal excessive filtering problem; (2) A new threshold function is designed by fusing the characteristics of the soft threshold function and the hard threshold function. The characteristic curve presents a smooth and gradually increasing characteristic of concave function, which solves the problem of large deviations in reconstructed signals. The experimental comparison of signal-to-noise ratio and root mean square error shows that the designed method in this paper can improve the accuracy of signal recognition and has higher application value in processing one-dimensional non-stationary signals.
Chest X-ray Segmentation Using Watershed and Super Pixel Segmentation Technique

Image segmentation is one of the key elements in image processing. Image segmentation is a way of acquiring hidden aspects within an image. It is used in various applications like object recognition or image compression. There are specific image segmentation techniques, which segments the image into multiple segments. These techniques differentiate or make the group of image. This paper presents a new segmentation technique that improves the quality of the segmented image. Performance metrics like entropy and NIQE are used to calculate the performance.
Assessing Security of Cryptocurrencies with Attack-Defense Trees: Proof of Concept and Future Directions

Cryptocurrencies such as Bitcoin have been one of the new major technologies of the last decade. In this paper, we assess the security of Bitcoin using attack-defense trees, an established formalism to evaluate the security of systems. In this paper, our main contributions are as follows: (1) We provide an extended attack-defense tree model for attacks on Bitcoin. (2) We demonstrate the general usability of existing analysis methods for attack-defense trees in this context. (3) We highlight further research directions necessary to extend attack-defense trees to a full-fledged overarching model for security assessment.
Approximate Fast Foreground Colour Estimation

Compositing objects from one image or video onto another background is a classic computer vision problem. Compositing requires an estimate of the foreground object’s transparency, a topic of study known as Alpha Matting. However, less studied is the requirement of an estimate for the foreground object’s colours in transparent regions. Naively compositing without this leads to bleed-through of the original background. In this work, we propose an approximation for the multi-level foreground estimation technique from Germer et al. [1]. Our method can be implemented 11 lines of Python code and yields comparable results to state of the art approaches while also being faster. Our code is available at https://github.com/PhotoRoom/fast-foreground-estimation.
SCD based Video Watermarking by Using Wavelet Transform and SVD

Embedding the Watermark in all frames of cover video is not a good practice in terms of complexity . This paper proposed the method in which the watermark will not be inserted into all frames of cover video. Due to the frequent illegal copying of digital media like image , video and audio. Watermarking method is playing an important approach for the purpose of copy right protection. This paper propose SCD method which enables to embed the watermark in selected frames of cover . Use of wavelet transform technique and singular value decomposition makes the approach robust against various attacks . The performance measurement of the algorithm shows the robustness against intentional and unintentional attacks.
Assessing the sovereignty and security of the Austrian internet

With many people depending on the internet in their daily work lives, the question on dependencies of these services arises. This is especially true when considering services provided by critical (IT) infrastructure or linked to governmental bodies. In this work, we provide a methodology and subsequent analysis results regarding the security and nature of dependencies of important Austrian sites on other nations and (potentially vulnerable) resources. Furthermore, we added a specific sub set to the analysis, focusing on governmental sites due to their increasing importance. The results of this analysis helps with the identification of critical resources outside the original services scope, especially considering the inclusion of services from outside the European Union and/or from unknown/unofficial sites. Furthermore, we also analyse the usage of security headers and the adoption of HTTPS, allowing an estimation of the state of security of vital services.
A Study of Web-based Blended Colleges and Universities English Teaching

Abstract The rapid development of modern information technology has created an extremely cheap premise for the communication of personal information. In this context, college English teaching is also facing unprecedented challenges and opportunities for innovation. This paper discusses the characteristics of college English teaching under the information network environment, puts forward some reform strategies to optimize the blended education, and gives some suggestions to combine the traditional English teaching mode with the network learning model.
Research and Application of Digital Collection Method of Human Movement

research-article Research and Application of Digital Collection Method of Human Movement Share on Authors: Yerong Hu Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Xiangzhen He Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Yihao Zhang Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Jia Zeng Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Huaiyuan Yang Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Shuaihang Zhou Northwest Minzu University, China Northwest Minzu University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 71Pages 1–6https://doi.org/10.1145/3469213.3470272Online:18 August 2021Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Forest Fire Analysis Based on InterCriteria Analysis

Forest fires annually affect large areas all over the world. They are one of the reasons for changes in the forest ecosystems, which lead to unpredictable consequences in long term. The present research extends and builds up an analysis of the forest fires risk assessment in Bulgaria over the last 20 years. Two methodologies, different in their essence, are used in the study: a common approach (Lubenov’s methodology) and InterCriteria Analysis (ICrA). Lubenov’s methodology classifies the different regions of Bulgaria in groups according to the risk of forest fires. ICrA, which seeks to find relations between some predefined criteria, is used as an additional approach to refine this classification. The research is based on the number of the forest fires occurred in different regions of Bulgaria and the size of the burned areas over the past 20 years. Expanding the period over which the study is conducted aims for the Lubenov’s methodology to confirm the results of ICrA.
Semantic Hierarchy Preserving Deep Hashing for Large-Scale Image Retrieval

Deep hashing models have been proposed as an efficient method for large-scale similarity search. How-ever, most existing deep hashing methods only utilize fine-level labels for training while ignoring the natural semantic hierarchy structure. This paper presents an effective method that preserves the classwise similarity of full-level semantic hierarchy for large-scale image retrieval. Experiments on two benchmark datasets show that our method helps improve the fine-level retrieval performance. Moreover, with the help of the semantic hierarchy, it can produce significantly better binary codes for hierarchical retrieval, which indicates its potential of providing more user-desired retrieval results. The codes are available at https://github.com/mzhang367/hpdh.git.
A Framework of Data Augmentation While Active Learning for Chinese Named Entity Recognition

Named entity recognition (NER) is a basic task to construct knowledge graph. The training performance is limited with few labelled data. One solution is active learning, which can achieve ideal results by multi-round sampling strategy to augment unlabelled data. However, there is very few labelled data in the early rounds, which leads to slow improvement on training performance. We thus propose a framework of data augmentation while active learning. To validate our claims, we focus on Chinese NER task and carry out extensive experiments on two public datasets. Experimental results show that our framework is effective for a series of classical query strategy. We can achieve 99% of the best deep model trained on full data using only 22% of the data on Resume, 63% labelled data is reduced as compared to pure active learning (PAL).
Comparative Assessment of Existing Meaningful Image Encryption Techniques

Abstract In recent trends, digital images are commonly used for communication, storage, medical imaging, etc. These images may contain confidential data. Image encryption and steganography are popular techniques to provide information security. In image encryption, a secret image is encoded using an encryption key, and in image steganography, a secret image is embedded in some cover media. Encrypted images are visually recognizable as these are noise-like, but stego images are visually unrecognizable. In this concern, some visually secure encryption techniques are developed, known as Meaningful Image Encryption (MIE). It includes the advantages of both image encryption and steganography. This paper provides a thorough review of existing MIE techniques. A comparative assessment based on various performance parameters is presented. Further, this paper provides the main application areas of MIE techniques. Moreover, the future research perspective to enhance the existing MIE techniques are discussed in detail.
Design and Development of Novel Ensemble Classifier for Visual Sentiment Analysis

Abstract Visual media is now become one of the pre-eminent methods utilized for conveying feelings or suppositions on the web. A huge number of photographs are being transferred by individuals on popular social network platform for interpersonal communication. Visual sentiment analysis (VSA) has pulled in wide consideration since an ever increasing number of individuals will communicate their feeling and conclusions through visual substance via online media. The domain of visual emotion evaluation is impractical because-of the great degree of discrimination in the human realization measure. In this paper, a novel ensemble classifier for visual sentiment analysis, which will use few labelled training specimens to attain an effective SA model, is proposed. The classified output is provided by the Ensemble classifier. The steps adopted in the proposed Visual sentimental analysis for the effective classification are preprocessing and the classification. The pre-processing is accomplished by the Resnet-101, which will boost the potential of the classifier. The effectiveness of the proposed sentimental analysis method is revealed by the comparative analysis and it is expected that the proposed method outperforms all the comparative methods. Then, broad datasets enable the quick advancement of profound neural organizations for recognition process. However, the explanation of enormous scope datasets is over the top expensive and tedious. The efficacy of the proposed sentimental analysis method will be revealed by the comparative analysis and it is expected that the proposed method will exceed all the comparative methods.
Acquiring Robot Navigation Skill with Knowledge Learned from Demonstration

The ability for robots to acquire navigation skill is a hotspot in the field of mobile robot research. In this work, to accelerate the process of learning mapless navigation skill for mobile robots, knowledge from demonstrations are used. What’s more, not only the success rate of navigation, but also the time cost and motion stability are taken into consideration. We propose a mobile robot navigation approach, which first learns the navigation knowledge from demonstrations, and then learns the navigation skill through exploration based on the learned knowledge. The knowledge is learned from demonstrations sampled from existing algorithms, rather than experts, to reduce the complexity of sampling. The reward function is specially designed to guide the robot to move towards the target efficiently and stably. Experimental results show that the proposed method can not only improve the learning efficiency, but also improve the navigation performance.
Academic ICT Research for Defence and Security

This chapter presents research activities in the interest of defence and security, conducted in the Bulgarian Academy of Sciences, and their main results. The uniquely formed capacity is applied in largely demand-driven research to support defence planning and policy-making, crisis management and critical infrastructure protection, cybersecurity and countering hybrid threats. The research methods are multidisciplinary and draw, in addition to technology-related subjects, from the fields of security studies, operations research, foresight, systems analysis, risk management, complex systems, multi-criteria decision making, and governance studies. The concluding section presents obstacles to and drivers for the future development of the academic ICT defence and security-related research and the outlines of a strategy to make this development sustainable in the long run.
Efficient global stereo-matching method of general images under a long baseline based on baseline estimation

For three-dimensional (3D) measurement of a large object, stereo matching needs to be done under a relatively long baseline in order to reduce the accumulated errors caused by the splicing process, but this brings new challenges to matching efficiency. The images under a short baseline have an advantage avoiding a very large disparity level that is usually presented in a large baseline to improve efficiency, whether the image pairs to be matched are of translation or rotation configuration. This paper mainly focuses on the general case existing for both, and the purpose is to efficiently execute a stereo matching in this scene based on short baseline estimation. A rectification method of three images used for baseline estimation is first studied to make the image planes become coplanar, which removes the vertical disparity. The three rectified images can form a stereo pair with a short baseline and the other one has a long baseline. Then the relationship that exists between disparities of a pixel on rectified stereo pairs with different baselines is deduced by considering the quantization error introduced in the projection and rectification. In this way, the disparity search range of a pixel on the rectified stereo pair with the long baseline is reduced from the usual hundreds of pixels to a few pixels predicted by its disparity with the short baseline, which greatly improves the calculation efficiency and makes it especially suitable for the 3D measurement of large objects. Experimental results are presented to demonstrate the efficiency and accuracy of the method.
Decomposition of Longitudinal Deformations via Beltrami Descriptors

We present a mathematical model to decompose a longitudinal deformation into normal and abnormal components. The goal is to detect and extract subtle abnormal deformation from periodic motions in a video sequence. It has important applications in medical image analysis. To achieve this goal, we consider a representation of the longitudinal deformation, called the Beltrami descriptor, based on quasiconformal theories. The Beltrami descriptor is a complex-valued matrix. Each longitudinal deformation is associated to a Beltrami descriptor and vice versa. To decompose the longitudinal deformation, we propose to carry out the low rank and sparse decomposition of the Beltrami descriptor. The low rank component corresponds to the periodic motions, whereas the sparse part corresponds to the abnormal motions of a longitudinal deformation. Experiments have been carried out on both synthetic and real video sequences. Results demonstrate the efficacy of our proposed model to decompose a longitudinal deformation into regular and irregular components.