Reviewerly/exHarmony huggingface dataset:
Old Photos Restoration by Using VAE

VAE is a generative model that “provides a probabilistic description of observations in potential Spaces”. Put simply, this means that VAE stores potential attributes as probability distributions. The idea of variational auto-encoders or VAE is deeply rooted in the methods of variational DB Bayesian and graphical models. This piece of work will discuss VAE Structure, VAE Loss Function, VAE Translation, and our final effects.
Architecture of Virtual Reality Systems and Metaverse

This chapter is dedicated to the foundations of virtual reality systems, emphasizing the distributed aspect. Communication, concurrency, and data sharing are analyzed. The second part of the chapter emphasizes the online worlds and the metaverse, including avatars and the role of Internet of Things.
Approximation model based on LSTM for predicting the next prime number in an infinite sequence

Prime numbers are a special set of natural numbers that have captured the attention of mathematicians since ancient times. As prime numbers are a fundamental component in many areas of mathematics, they have naturally found wide applications in various fields of knowledge, such as cryptography. The goal of all researchers is to discover the distributional relationships within this infinite set of numbers or, at the very least, to create a mathematical model for predicting the next prime number in a diverging sequence. This article is dedicated to an attempt at solving this problem based on a deep learning model -Long Short-Term Memory (LSTM) neural network.
Cultivation of Labour Values in Schools and Universities in the AI Era: Logic, Problem, and Strategy

The artificial intelligence (AI) era has posed a great challenge to the cultivation of labour values in schools and universities, while labour values, whose theoretical logic originates from the dialectical unity relationship between the subject and object of labour, are the core in labour education. Labour values are embodied by labour subjectivity and developed by labour practicality. The research takes the bidirectional relationship between AI and labour education as the theoretical perspective to locate three major problems confronted by labour education: (1) educational administration in schools and universities; (2) separation between technique and morality; (3) binary opposition between mental and manual labour. In this case, labour education in schools and universities should adhere to the proper value orientation of labour education, promote the inter-coordination between labour skills and labour values, and pursue a system that integrates the five education dimensions—morality, intelligence, physique, aesthetics, and labour.
Research on the Mechanism of Training Innovation Ability for Chinese Postgraduates

Training postgraduate students’ innovation ability is an important task of graduate education. The article elaborates on the innovation ability of postgraduate students from three aspects: innovation awareness, innovative thinking and innovative skills. It deeply analyses the problems in the innovation training system of postgraduate students, such as insufficient innovation ability of postgraduates for themselves, insufficient guidance from postgraduate supervisors, incomplete talent training plans, and insufficient scientific research and practical abilities. Finally, a mechanism system for cultivating the innovation ability of Chinese graduate students is established from the aspects of creating a good institutional environment, highlighting the mechanism of postgraduates as the main part, improving the responsibility mechanism of postgraduate supervisors, improving the teaching mechanism of postgraduate education, constructing the evaluation mechanism of postgraduate education, and improving the innovation practice mechanism of postgraduates.
The role of the audience in contemporary data art

As a new form of contemporary art, data art has changed the audience’s artistic experience. This paper first introduces the characteristics of data art which are different from other contemporary art. Secondly, in the context of public participation in art, it explores how data art enables audiences to participate in various stages of artistic creation, so that audiences can become one of the creators rather than mere art connoisseurs. Finally, it summarizes participatory issues in different stages of data art.
The geographical information system toolbox for mineral exploration

Mineral exploration is a multifaceted process aiming at narrowing down the search space for discovering new mineral deposits. Conducting several surveys, be they geochemical, geological, or geophysical, is a prerequisite to selecting seemingly favorable exploration targets. The more data one can achieve, the more certain the exploration targets will become. However, given the complexity of ore-forming processes and considering the premise that buried, covered, and deep-seated mineral deposits usually do not show strong geochemical and geophysical signatures, the output of different surveys should be compiled and integrated into a series of maps showing the potential of discovering mineral deposits. Geographical information system (GIS) systems can provide support to fulfilling the previous goal. Geological and mathematical frameworks constitute two key aspects of GIS-based mineral exploration, covered in this chapter. Also, in this chapter, we will acknowledge that GIS-based mineral exploration is not without untold risk; that is, different uncertainties linked to GIS-based processes that eventually propagate into the exploration targets will be discussed in this chapter.
Methods of eliminating homonymy within different, grammatically similar word groups

The problem of automatic processing of natural language remains relevant for more than half a century. One of the important problems in the field of NLP is the creation of a semantic analyzer, which in turn goes through a number of steps. Determining homonymy is important in the semantic analysis of sentences. A method based on rules, a method based on statistical data, and methods based on machine learning are also used to determine homonymy. Statistical methods are mainly used to determine homonymy between grammatically similar word groups. In this article discusses the use of homonymy between two grammatically similar nouns and adjectives using statistical methods, namely Frequency and Bayesian methods. If bigrams and trigrams are used in the Bayesian method, the characteristics of word groups are classified in the frequentist method, and the parameters that can distinguish them are determined. The identified parameters are converted into numbers as a result of observations and probabilistic decisions are made.
Remote sensing for mineral exploration

This chapter will focus on spectral features of hydrothermal alteration minerals and lithologies in the visible and near-infrared, shortwave infrared, and thermal infrared wavelength regions. The technical characteristics of multispectral and hyperspectral sensors, synthetic aperture radar, and unmanned aerial vehicles will be deciphered for detecting the alteration minerals and zones, lithological units, and structural features associated with ore mineralizations. Explanations about data acquisition for the remote sensing data, preprocessing techniques, image-processing algorithms, accuracy assessment techniques, interpretation of remote sensing data for alteration mineral detection, lithological mapping, structural analysis, and case studies will be discussed in this chapter.
Exploring the Problems of Applying Intelligent Media in Chinese Journalism Based on the Perspective of Artificial Intelligence

With the rapid development of science and technology, artificial intelligence has gradually entered the field of people’s lives, and human society has entered the era of intelligence. Among them, the application of artificial intelligence in the field of news communication is also becoming more and more extensive. Intelligent media technology can help news media acquire information, analyze data, generate content, and improve the efficiency and quality of news dissemination more quickly and accurately. Traditional news gathering requires manual browsing of a large number of information sources to filter out valuable information. Artificial intelligence technology can quickly extract useful content from a large amount of information through natural language processing, machine learning, and other technologies, providing more comprehensive and accurate information to the news media. At the same time, the emergence of artificial intelligence also brings some disadvantages to the news industry, for example, machine news writing is limited by technical rules, which affects the existence of its limitations. This paper will therefore discuss three aspects of the changes brought about by AI in the media industry - the structure of communication, communication technology, and communication philosophy - as well as the ethical and legal issues involved in AI in the media industry. Firstly, the researcher aims to understand the problems in the application of AI in the field of journalism and communication. Secondly, the research theory used in this article is the diffusion of innovation theory. Developed by Rogers in the 1960s, this theory states that some people will be more open-minded and more willing to accept innovations than others when it comes to innovation. Again, the research method used in this article is a qualitative research method of web-based text analysis. Ultimately, the researcher identified three problems: the content of the research was superficial, the emergence of smart media broke the traditional media compliance under ethical laws, and the inadequate mechanism for nurturing talent. Based on these problems, the researcher proposed solutions, which were to conduct an in-depth analysis of the application of artificial intelligence in the communication industry, to develop laws and regulations that comply with the application of intelligent media in the communication field, and to train high-end talents who can use artificial intelligence.
Automotive Cybersecurity: The gap between theoretical risks and real attacks on your vehicle

In 2022, over 250 cybersecurity incidents in the automotive sector have been reported by Upstream’s AutoThreat® researchers. Despite the advent of the UN R155 regulation and the increasing budget dedicated to product cybersecurity, hackers still manage to breach vehicles. Does it mean that all these efforts are in vain, and attacks are inevitable? Not exactly. Concepts like security-by-design and zero trust are more and more taken into consideration during the product development phase. The processes now include risk analysis, security concepts and other security artefacts, as well as security controls that are implemented and tested. However, despite this approach and new mindset, the main objective may be missed: the vehicle must be protected against attacks in the field, so the risks must be mitigated in practice. This is the real challenge that should guide all car manufacturers’ actions. Current product security is a one-way street towards the finished car. They lack the integration of technologies and processes that allow for feedback and experiences from the field to be reflected during development. We will highlight the potential root causes to this mismatch between regulation side effects, human influence factor in processes and lack of indicators to measure the real risks in the field. Finally, we will offer an approach to tackle this issue and to close the gap between theory and practice in risk management, while reducing security costs.
Improving pedestrian segmentation using region proposal-based CNN semantic segmentation

Pedestrian segmentation is a critical task in computer vision, but it can be challenging for segmentation models to accurately classify pedestrians in images with challenging backgrounds and luminosity changes, as well as occlusions. This challenge is further compounded for compressed models that were designed to deal with the high computational demands of deep neural networks. To address these challenges, we propose a novel approach that integrates a region proposal-based framework into the segmentation process. To evaluate the performance of the proposed framework, we conduct experiments on the PASCAL VOC dataset, which presents challenging backgrounds. We use two different segmentation models, UNet and SqueezeUNet, to evaluate the impact of region proposals on segmentation performance. Our experiments show that the incorporation of region proposals significantly improves segmentation accuracy and reduces false positive pixels in the background, leading to better overall performance. Specifically, the SqueezeUNet model achieves a mean Intersection over Union (mIoU) of 0.682, which is a 12% improvement over the baseline SqueezeUNet model without region proposals. Similarly, the UNet model achieves a mIoU of 0.678, which is a 13% improvement over the baseline UNet model without region proposals.
Cybercrime on the Ethereum Blockchain

We propose a taxonomy of cybercrime on the Ethereum blockchain and examine how cybercrime impacts victims’ risk-taking and returns. Our difference-in-differences analysis of a sample of victims and matched non-victims suggests that victims increase their long-term total risk-taking and earn lower risk-adjusted returns in the post-cybercrime period. Victims’ long-term total risk-taking increases because they increase diversifiable risk in the long term. The increased diversifiable risk correlates with victims’ withdrawal from altcoins after cybercrime. At the same time, the reduction in risk-adjusted returns correlates with increased trading activity and churn, due plausibly to managing cybercrime exposure. In the cross-section of Ethereum addresses, we show that the most-affluent victims take a systematic approach to restore their pre-cybercrime wealth level, while the least-affluent victims turn into gamblers. Finally, a parsimonious forensic model explains a good part of the addresses’ probability of being involved in cybercrime, both on the victim and the cybercriminal side.
Rapid Prediction of Breast Biomechanics Under Gravity Loading Using Surrogate Machine Learning Models

Identifying and localising breast tumours is challenging due to differing patient positions between clinical examination and treatment procedures. Individual-specific, physics-driven modelsModel of the breast can help clinicians track and co-locate information between medical images acquired from different modalities but are unsuitable for real-time intervention. We present a surrogate machine learning modelModel that predicts the breast’s mechanical behaviour under gravity loading in near real time. Synthetic data was generated from a physics-driven finite element (FE) modelModel simulating material point displacements d of the breast, using a reference geometry and combinations of a constitutive material parameter C1 and the relative orientation of the breast with respect to gravity g. The best-trained surrogate modelModel produced an error of 0.13 mm ± 0.03 mm with respect to the L2-norm of the FE modelModel’s displacement field in modelModel validation. This modelModel can predict the material point displacement of the breast in clinically relevant positions (e.g. prone and supine) with a maximum root mean squared error of 1.04 mm (SD 1.08 mm). The computations took around 2.0–2.1 s, up to 82.5 times faster than the equivalent FE modelsModel on a CPU. The proposed approach is therefore promising for developing augmented reality tools to aid with real-time clinical navigation of soft tissues.
Evaluation metrics for video captioning: A survey

Automatic evaluation metrics play an important role in assessing video captioning systems. Popular metrics used for assessing such approaches are based on word matching and may fail to evaluate the quality of automatically generated captions due to inherent natural language ambiguity. Moreover, they require many reference sentences for effective scoring. With the fast development of image and video captioning methodologies using deep learning in recent years, many metrics have been proposed for evaluating such approaches. In this study, we present a survey of automatic evaluation metrics for the video captioning task. Moreover, we highlight the challenges in evaluating video captioning and propose a taxonomy to organize the existing evaluation metrics. We also briefly describe and identify the advantages and shortcomings of those metrics and identify applications or contexts in which these metrics can be better used. To identify the advantages and limitations of the evaluation metrics, we quantitatively compare them using videos from different datasets employed for the video description task. Finally, we discuss the advantages and limitations of the metrics and propose some promising future research directions, such as semantic measurement, explainability, adaptability, extension to other languages, dataset limitations, and multimodal free-reference metrics.
Followership behavior, team performance and employee satisfaction: Inputs to employee satisfaction model

Using a combination of theoretical constructs and empirical analysis, this study follows the logical line of "followership behavior -team performance -employee satisfaction" and conducts an in-depth analysis and discussion of the mechanisms of employee followership and employee satisfaction respectively.Through the empirical analysis of the questionnaire data from 318 full-time employees of the company, using statistical analysis software such as SPSS22.0 and AMOS22.0, and strictly following the basic paradigm of empirical research, this study obtained the following findings: (1) The respondents have negative followership behavior in terms of supporting leader decision, solving problems for leader and upward influence behavior.(2) The team have poor performance in terms of cost efficiency, project quality and task completion.(3) The employees have low level of employee satisfaction in terms of salary and benefits, working condition and promotion and recognition.(4) There is a high significant correlation between followership behavior, team performance and employee satisfaction.(5) Framework for improving employee satisfaction was develop.
Modified rat swarm optimization with deep learning model for robust recycling object detection and classification

Biomass residues consist of sewage effluents and sludges, crop wastes, non-recyclable municipal solid waste industrial and domestic greywater, and much more. Recycling is regarded as one such significant disposal technique. Considering the intelligent classification and detection of solid waste as an essential factor in consumption and recycling, multi-object solid waste identification and classification methodology on the basis of transfer learning (TL) are presented. This study develops a Modified Rat Swarm Optimization with Deep Learning for Robust Recycling Object Detection and Classification (MRSODL-RODC) model. Primarily, fully convolutional network (FCN) is applied for the identification of waste objects. Moreover, MRSO with deep belief network (DBN) model is applied for object detection process. The design of the MRSO algorithm for the DBN technique, showing the novelty of our work. The performance validation of the MRSODL-RODC model can be executed with the help of the benchmark dataset from Kaggle repository. The experimental outcomes demonstrated the better performance of the MRSODL-RODC model over recent approaches with higher accuracy of 99.20%.
Optical diffractometry by rough phase steps

Optical diffractometry (OD) using a phase step is an alternative for interferometry, further, has least sensitivity to environmental vibrations. Therefore, OD has found numerous interesting metrological and technological applications. OD utilizes a phase step to detect the influence of objects under measurement by the changes in the Fresnel diffraction pattern. Recently, we showed that such measurements do not require infinitively sharp phase steps, although fabrication of such sharp elements is also impossible. Here, we address the issue of smoothness of the phase step surfaces. So far, in all of the OD applications the surfaces of the incorporated phase steps are considered to be optically smooth and flat. However, practically, some amount of roughness and unflatness is unavoidable even in precise and careful fabrication process. We show that preserving the OD-diffraction-pattern characteristics of a phase step depends on the level of roughness in the surfaces of the phase step. We define number of detectable fringes and autocorrelation functions of the diffraction patterns as the measures for evaluating the similarity of the rough phase step diffractions to the ideal case. We derive the theoretical description and confirm the results with simulations and experiments.
Self-supervised embedding for generalized zero-shot learning in remote sensing scene classification

Generalized zero-shot learning (GZSL) is the most popular approach for developing ZSL, which involves both seen and unseen classes in the classification process. Many of the existing GZSL approaches for scene classification in remote sensing images use word embeddings that do not effectively describe unseen categories. We explore word embedding to describe the classes of remote sensing scenes to improve the classification accuracy of unseen categories. The proposed method uses a data2vec embedding based on self-supervised learning to obtain a continuous and contextualized latent representation. This representation leverages two advantages of the standard transformer architecture. First, targets are not predefined as visual tokens. Second, latent representations preserve contextual information. We conducted experiments on three benchmark scene classification datasets of remote sensing images. The proposed approach demonstrates its efficacy over the existing GZSL approaches.
Towards an adversarially robust convolutional neural network for automated diagnosis of malaria infection from microscopy images

Malaria is a life-threatening mosquito-borne disease of global importance, and it is most prevalent in the low-income countries of the developing world. The diagnosis of malaria infection is conventionally performed by laboratorians through visual examination of blood smear images under a microscope. However, the results of such diagnostic testing depend on the laboratory technicians' experience with staining and image interpretation, which can be severely lacking in low-resource settings. Convolutional neural networks (CNNs) have great potential for automated malaria diagnosis from images without the need of human expert interaction. However, studies have shown that current CNNs are not robust to adversarial noises which are small input perturbations that cause CNNs to incorrectly classify medical images. In this study, we developed a CNN for malaria parasite identification and evaluated it on a dataset of 27,558 cell images through patient-level cross validation. To improve robustness against adversarial noises for this application, the structure of the CNN was specially designed to limit the receptive field of the output, and adversarial training was applied with gradually increasing level of noises. Compared to three baseline networks (ResNet-18, MobileNet, and MnasNet) and two variants of the CNN, our custom-designed CNN achieved a substantially lower computation cost and better adversarial robustness against PGD-generated adversarial noises on the malaria image dataset. Our study holds the promise of developing a robust and affordable solution for automated diagnosis of malarial infection.