Reviewerly/exHarmony huggingface dataset:
The EU Artificial Intelligence Act: Regulating Subliminal AI Systems

AI in combination with other innovative technologies promises to bring unprecedented opportunities to all aspects of life. These technologies, however, hold great dangers, especially for the manipulation of the human mind, which have given rise to serious ethical concerns. Apart from some sectoral regulatory efforts to address these concerns, no regulatory framework for AI has yet been adopted though in 2021 the European Commission of the EU published a draft Act on Artificial Intelligence and UNESCO followed suit with a Recommendation on the Ethics of Artificial Intelligence.The book contextualises the future regulation of AI, specifically addressing the regulatory challenges relating to the planned prohibition of the use of AI systems that deploy subliminal techniques. The convergence of AI with various related technologies, such as brain–computer interfaces, functional magnetic resonance imaging, robotics and big data, already allows for "mind reading" or "dream hacking" through brain spyware, as well as other practices that intrude on cognition and the right to freedom of thought. Future innovations will enhance the possibilities for manipulating thoughts and behaviour, and they threaten to cause serious harm to individuals as well as to society as a whole.The issue of subliminal perception and the ability to deceive and manipulate the mind below the threshold of awareness causes severe difficulties for law and democracy and raises important questions for the future of society. This book shows how cognitive, technological, and legal questions are intrinsically interwoven, and aims to stimulate an urgently needed transdisciplinary and transnational debate between students, academics, practitioners, policymakers and citizens interested not only in the law but also in disciplines including computer science, neuroscience, sociology, political science, marketing and psychology.
E-Chain Learn: A Degree Certification System Based on Blockchain

Most of the existing academic certification systems are based on the traditional B/S structure and Web technology, and most of the functions and permissions are in the central server. The data may be unreliable due to data tampering and other reasons. In response to the above problems, this article uses the advantages of blockchain in decentralization, non-tampering, and traceability to construct a blockchain credit management platform for college student archives. Combining with smart contract technology and CA certification technology, the platform provides identity verification, degree certification, and performance certification involved in college student files in decentralized security pattern.
A survey of blockchain as a service platform

Blockchain provides more security and decentralised systems. However, it is difficult for startups and small companies to build and deploy their blockchain networks. So, cloud computing providers make it easy for those to work with blockchain using their networks and cloud infrastructure through using blockchain as a service platform. There are many blockchain as a service providers and platforms. This survey provides an overview of 23 blockchain-as-a-service platforms and provides a comparison based on provider, blockchain protocol, blockchain type, cost, security, and support of smart contract.
Monocular Depth Estimation using a Multi-grid Attention-based Model

With the increased use of depth information in computer vision, monocular depth estimation has been an emerging ﬁeld of study. It is a challenging task where many deep convolutional neural network-based methods have been used for depth prediction. The problem with most of these approaches is that they use a repeated combination of max-pooling and striding in an encoder, which reduces spatial resolution. In addition, these approaches use information from all the channels directly from the encoder, which is prone to noise. Addressing these issues, we present a multigrid attention-based densenet-161 model. It consists of a multigrid densenet-161 encoder that increases the spatial resolution and an attention-based decoder to select the important information from low-level features. We achieved absolute relative error (Absrel) of 0.109 and 0.0724 on NYU v2 and KITTI, dataset respectively. Our proposed method exceeded most evaluation measures with fewer parameters compared to the state-of-the-art on standard benchmark datasets. We produce a dense depth map from a single RGB image which can be used to create a dense point cloud. The anticipated depth map is accurate and smooth, which can be used in several applications.
Meaningful secret image sharing for JPEG images with arbitrary quality factors

JPEG is the most common format for storing and transmitting photographic images on social network platforms. JPEG image is widely used in people's life because of their low storage space and high visual quality. Secret image sharing (SIS) technology is important to protect image data. Traditional SIS schemes generally focus on spatial images, however there is little research on frequency domain images. In addition, the current tiny research on SIS for JPEG images only focuses on JPEG images with a compression quality factor (QF) of 100. To overcome the limitation of JPEG images in SIS, we propose a meaningful SIS for JPEG images to operate the quantized DCT coefficients of JPEG images. The random elements utilization model is applied to achieve meaningful shadow images. Our proposed scheme has a better quality of the shadow images and the recovered secret image. Experiment results and comparisons indicate the effectiveness of the scheme. The scheme can be used for JPEG images with any compression QF. Besides, the scheme has good characteristics, such as (k,n) threshold, extended shadow images.
Using Active Seat Belt Retractions to Mitigate Motion Sickness in Automated Driving

The introduction of automated-driving functions provides passengers with the opportunity to engage in non-driving related tasks during the ride. However, this benefit might be compromised by an increased incidence of motion sickness. Therefore, we investigated the effectiveness of active seat belt retractions as a countermeasure against motion sickness during inattentive automated driving. We hypothesized that seat belt retractions would mitigate motion sickness by supporting passengers to anticipate upcoming braking maneuvers, by actively tensioning their neck muscles and, thereby, reducing the extent of forward head movement while braking. In a motion base driving simulator, 26 participants encountered two 30 min automated drives in slow-moving traffic: one drive with active seat belt retractions before each braking maneuver and a baseline drive without. The results revealed that there was no difference in perceived motion sickness between both experimental conditions. Seat belt retractions resulted in an increased activity of the lateral neck muscles and supported drivers to anticipate braking maneuvers. However, at the same time, the retractions led to an increased magnitude of head movement in response to braking. This research lays the groundwork for future research on active seat belt retractions as a countermeasure against motion sickness and provides directions for future work.
Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network’s input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education.
REE geochemistry of garnet associated with the Gamsberg Zn-Pb deposit, South Africa

Garnet is a ubiquitous mineral in Fe-, Mn-, and Ca-rich exhalites associated with the 160 Mt Gamsberg deposit in South Africa. It occurs in distinct units of garnet-quartz rocks (coticules) below, above and lateral from the ore horizon, but is also plentiful in Fe-Mn calc-silicates, Ca-Mn marbles and chemogenic amphibole-sphalerite-pyrrhotite mineralisation. REE patterns of both whole rock and individual garnets indicate that these rock types consist predominantly of chemical precipitates with varying contributions of detrital material. A strong hydrothermal signature is evident from most garnets, resulting in a general enrichment of light REE. Positive Eu anomalies in the ore-bearing lithologies suggest a significant input of hot reduced brines, capable of transporting Zn and Pb, whereas Fe-Mn calc-silicates and impure marbles are characterized by relatively cool Fe-Mn-rich fluids. In contrast, coticules distal to the mineralisation exhibit the largest negative Eu anomalies, suggesting large contributions of aluminous clays to the precursor sediment and relatively more oxidizing basin conditions. A systematic increase in the size of the Eu anomaly as the ore body is approached is characteristic and might act as an exploration guide.
Fast 2-step regularization on style optimization for real face morphing

StyleGAN is now capable of achieving excellent results, especially high-quality face synthesis. Recently, some studies have tried to invert real face images into style latent space via StyleGAN. However, morphing real faces via latent representation is still in its infancy. Training costs are high and/or require huge samples with labels. By adding regularization to style optimization, we propose a novel method to morph real faces based on StyleGAN. To do the supervised task, we label latent vectors via synthesized faces and release the label set; then we utilize logistic regression to fast discover interpretable directions in latent space. Appropriate regularization helps us to optimize both latent vectors (faces and directions). Moreover, we use learned directions under different layer representations to handle real face morphing. Compared to the existing methods, our method faster yields a larger diverse and realistic output. Code and cases are available at https://github.com/disanda/RFM.
A Proactive Improvement Toward Digital Forensic Investigation Based on Deep Learning

This chapter presents a proactive improvement toward digital forensic investigation based on Deep Learning. Digital Forensics has witnessed exponential growth in the last few years. It builds the trust of users toward digital practices. However, efficient forensics technology is required for adequate security. Recently, deep learning has been widely used for accomplishing digital forensic tasks more accurately. It provides a much more efficient means of digital forensics. In this chapter, the various learning methods, along with their respective merits and demerits, are discussed. To accelerate the investigation process using digital forensics, the research deeply analyses the convolutional network with its available applications, including probe study of images and videos. The authentication process using image recognition and the exigency of the deep learning model is discussed. Moreover, the smart Internet of Things (IoT) concept, architecture, and the applicability of network forensics in IoT devices are introduced in the chapter.
The impact of soft leadership skills in achieving discrimination in human resources: a field study of the opinions of a sample of workers in the Salah El-Din Health Department

This study is concerned with identifying the role of soft leadership skills, whose dimensions consist of (communication skills, development and training skills, cooperation and teamwork skills, professional ethics and values practices) in promoting discrimination in human resources. The descriptive analytical approach was adopted, as the data were collected through the design of a survey questionnaire and distributed to the administrative staff in the Salah al-Din Health Directorate. Which numbered (63) forms, (61) were retrieved. I.e. (96.8%), which represents the approved sample of the study valid for statistical analysis. The data was analyzed according to the statistical analysis program (SPSS.V22). One of the most important findings of the study: that the answers of the research sample were close to agreement with the paragraphs of soft leadership, and this result reflects the reality of the interest of the Salah El-Din Health Directorate in soft leadership. The study made several recommendations, including: The necessity of developing soft leadership in a way High in order to influence others, and to avoid any changes that occur in the future, such as (Covid 19) and others.
Preservice Training Strategies and Guarantee Mechanisms for Primary and Secondary School Teachers in the Development of Smart Cities by the Internet of Things

It aims to perfect the preservice training system and improve the teaching quality of primary schools. The research methods are questionnaire survey and interview, and 154 teachers from 16 primary schools under the background of smart city development are used as the research objects. Questionnaires are distributed to them, and the Internet of Things (IoT) technology is used to analyze the adaptation law of primary school teachers’ preservice training and to conduct in-depth research on teacher education and training strategies according to the relevant curriculum system. The results manifest that the IoT technology can effectively process the questionnaire data. From the perspective of the smart city, more than 70% of art teachers and physical education (PE) teachers know nothing about their professional level, and nearly 40% of music teachers know little about their professional level of teachers. In addition, more than 50% of the teachers surveyed do not have a clear understanding of their own professional standards, teacher training standards, primary school training strategies, and guarantee mechanisms of the teaching effect. It means that teachers in the progress of smart cities should strengthen the training of professional level and education policy during preservice training. After the evaluation and training system is established according to the ratio of “25.4% of primary school students’ growth knowledge +42.7% of professional knowledge +31.9% of teacher education curriculum knowledge”, students’ learning enthusiasm increased by 23%, and their academic performance increased by 15%. This indicates that the training system can ensure the good learning effect of students. Therefore, the research on preservice training strategies and guarantee mechanisms for primary and secondary school teachers in smart cities by the IoT provides a certain direction for subsequent teacher training.
A Structure Preservation and Denoising Low-Light Enhancement Model via Coefficient of Variation

In this paper, we propose a structure-preserving and denoising low-light enhancement method that uses the coefficient of variation. First, we use the coefficient of variation to process the original low-light image, which is used to obtain the enhanced illumination gradient reference map. Second, we use the total variation (TV) norm to regularize the reflectance gradient, which is used to maintain the smoothness of the image and eliminate the artifacts in the reflectance estimation. Finally, we combine the above two constraint terms with the Retinex theory, which contains the denoising regular term. The final enhanced and denoised low-light image is obtained by iterative solution. Experimental results show that our method can achieve superior performance in both subjective and objective assessments compared with other state-of-the-art methods (the source code is available at: https://github.com/bbxavi/SPDLEM .).
Quantumness and Dequantumness Power of Quantum Channels

Focusing on the dynamics of quantumness in ensembles, we propose a framework to qualitatively and quantitatively characterize quantum channels from the perspective of the amount of quantumness in ensembles that a quantum channel can induce or reduce. Along this line, the quantumness power and dequantumness power are introduced. In particular, once a quantum dynamics described by time-varying quantum channels reduces the quantumness for any input ensembles all the time, we call it a completely dequantumness channel, whose relationship with Markovianity is analyzed through several examples.
Recaptured attack-resilient watermarking scheme

We propose a recaptured attack-resilient blind watermarking scheme. The work aims to recover the watermark information from the stego (watermarked image) reacquired through a digital liquid-crystal display monitor. In the age of digital globalization, problems like copyright and integrity infringement of digital content are growing exponentially. The content owner can prove their ownership by extracting the embedded watermark from the digitally breached data. The presented framework is robust to several geometrical and signal processing attacks, including lens distortions, intensity distortion, and moiré distortion caused by recapture attack. The singular values of a watermark image and a unique security key are embedded in the host image’s double-density dual-tree discrete wavelet transform (DDD-DWT) coefficients. However, most singular value decomposition (SVD) based watermarking algorithms are prone to false alarm rates and missed alarm rates, causing a security issue. On that account, the proposed watermarking scheme in this paper introduces a unique security key embedding for authentication at the decoder side. The experimental results show that the proposed framework provides high perceptibility with a WPSNR value >59 dB and robustness against various attacks and security.
Digitization and Public Trust in Health System During COVID Pandemic

COVID-19 pandemic has compelled governments worldwide to curb public services and activities by imposing strict lockdowns and limiting in-person interactions. Restrictions in daily lives have impacted citizens both financially and mentally. Availability of essential services through digital channels and social media interactions has provided some respite to businesses and individuals during these challenging times. It has long been argued that the digitization of public services brings transparency and increases public trust in the government. Digital trust promotes the usage of online services, which could translate to pandemic-related guidelines and information shared by health authorities in a country. Trust in online channels during pandemics has the potential to improve compliance with health-related policies. However, inadequate data privacy and security regulations may deteriorate the public's trust and deter the usage of digital channels for public services or disease surveillance. Using real-world survey data and macroeconomic indicators, this study empirically investigates the role of digitization of public services and their use on the trust and compliance to health system during a pandemic. The study's findings should encourage governments to improve public confidence in digital services to fight ongoing and upcoming pandemics.
Unsupervised disparity estimation from light field using plug-and-play weighted warping loss

We investigated disparity estimation from a light field using a convolutional neural network (CNN). Most of the methods implemented a supervised learning framework, where the predicted disparity map was compared directly to the corresponding ground-truth disparity map in the training stage. However, light field data accompanied with ground-truth disparity maps were insufficient and rarely available for real-world scenes. The lack of training data resulted in limited generality of the methods trained with them. To tackle this problem, we took a simple Figure-and-play approach to remake a supervised method into an unsupervised (self-supervised) one. We replaced the loss function of the original method with one that does not depend on the ground-truth disparity maps. More specifically, our loss function is designed to indirectly evaluate the accuracy of the disparity map by using warping errors among the input light field views. We designed pixel-wise weights to properly evaluate the warping errors in the presence of occlusions, and an edge loss to encourage edge alignment between the image and the disparity map. As a result of this unsupervised learning framework, our method can use more abundant training datasets (even those without ground-truth disparity maps) than the original supervised method. Our method was evaluated on computer-generated scenes (4D Light Field Benchmark) and real-world scenes captured by Lytro Illum cameras. Our method achieved the state-of-the-art performance as an unsupervised method on the benchmark. We also demonstrated that our method can estimate disparity maps more accurately than the original supervised method for various real-world scenes.
Enhanced Computer Vision using Automated Optimized Neural Network Image Pre-processing

This research focuses on the benefits of computer vision enhancement through use of an image pre-processing optimization algorithm in which numerous variations of prevalent image modification tools are applied independently and in combination to specific sets of images. The class with the highest returned precision score is then assigned to the feature, often improving upon both the number of features captured and the precision values. Various transformations such as embossing, sharpening, contrast adjustment, etc. can bring to the forefront and reveal feature edge lines previously not capturable by neural networks, allowing potential increases in overall system accuracy beyond typical manual image pre-processing. Similar to how neural networks determine accuracy among numerous feature characteristics, the enhanced neural network will determine the highest classification confidence among unaltered original images and their permutations run through numerous pre-processing and enhancement techniques.