Reviewerly/exHarmony huggingface dataset:
Neural map style transfer exploration with GANs

Neural Style Transfer is a Computer Vision topic intending to transfer the visual appearance or the style of images to other images. Developments in deep learning nicely generate stylized images from texture-based examples or transfer the style of a photograph to another one. In map design, the style is a multi-dimensional complex problem related to recognizable visual salient features and topological arrangements, supporting the description of geographic spaces at a specific scale. The map style transfer is still at stake to generate a diversity of possible new styles to render geographical features. Generative adversarial Networks (GANs) techniques, well supporting image-to-image translation tasks, offer new perspectives for map style transfer. We propose to use accessible GAN architectures, in order to experiment and assess neural map style transfer to ortho-images, while using different map designs of various geographic spaces, from simple-styled (Plan maps) to complex-styled (old Cassini, Etat-Major, or Scan50 B&W). This transfer task and our global protocol are presented, including the sampling grid, the training and test of Pix2Pix and CycleGAN models, such as the perceptual assessment of the generated outputs. Promising results are discussed, opening research issues for neural map style transfer exploration with GANs.
NFTs Emergence in Financial Markets and their Correlation with DeFis and Cryptocurrencies

Non-fungible tokens (NFT) have been defined as digital assets that encode items such as art, collectables, and in-game goods. They are often stored in smart contracts on a blockchain and are exchanged online, frequently using Bitcoin. As NFT became increasingly popular in the last few years, decentralized financial assets (DeFi) tokens also started receiving growing attention as financial instruments that differ from NFTs and cryptocurrencies. Based on data on NFTs, DeFi tokens, and cryptocurrency daily prices between January 15th and December 6th, 2021, we examine the correlation between NFTs, DeFi tokens and major cryptocurrencies such as Bitcoin and Ethereum. Using the volatility spillover matrix approach by Diebold and Yilmaz (2012) as applied by Dowling (2021) and including DeFis into the discussion, we find that there is very limited spillover to and from non-traditional financial markets. Also, DeFi assets appear to be relatively unconnected to cryptocurrency markets. Following the methodology by Karim, Lucey, Naeem and Uddin (2021) of the quantile connectedness approach and the cross-quantilogram model of Han, Linton, Oka and Whang (2016), we determine that positive DeFi and Crypto spillovers exceeded negative NFT spillovers. This paper concludes that both NFTs and DeFi assets show significant potential in terms of portfolio diversification since they display low correlation with cryptocurrencies, especially in the case of DeFis thanks to it being disconnected from other assets in the market, based on this year's data. This has significant implications for investors who seek to diversify their portfolios by including cryptocurrency, NFTs and DeFis as assets.
Tinker, Tailor, Twitter, Lie: Government Disinformation and Freedom of Expression in a Post-Truth Era

Abstract The spread of disinformation has received significant attention in recent years, yet little has been paid to government disinformation, and whether governments may violate freedom of expression not only in how they regulate disinformation, but also in how they facilitate, sow and spread it. This article analyses whether and to what extent Article 10 of the ECHR is engaged by government disinformation. It extends the analysis from well-established violations of freedom of expression—overt censorship and withholding information—into novel forms of government interference in the ‘post-truth’ age: false claims of ‘fake news’ levelled at the press and intentional lies about matters of public importance. These latter categories warrant further attention, as governments can cause just as much harm to public discourse and debate by intentionally injecting falsehoods as by censoring truth. A purposive approach to freedom of expression is needed to protect not only the means of expression, but also the ends—vibrant democratic discourse and meaningful public debate.
Students’ Perspective of Online Learning on Speaking Class during Covid-19 Pandemic

All educational institutions during COVID-19 pandemic move to online learning to prevent the spread of this virus. All students and lecturers of the institutions have no other option. They must use some of online platforms to continue the learning process. The aim of this research was to know the students’ perspective of online learning on speaking class during COVID-19 pandemic. This research was categorized as mixed method research. There were 20 items of the questioner that have been distributed. The result showed that 14 items disagree and 6 items agree.
Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

Insulator defect detection is an important task in inspecting overhead transmission lines. However, the surrounding environment is complex, and the detection accuracy of traditional image processing algorithms is low. Therefore, insulator defect detection is still mainly performed manually. In order to improve this situation, we proposed an insulator defect detection method called INSU-YOLO based on deep neural networks. Overexposure points in the image will interfere with insulator detection, so we used image augment to reduce noise and extract the edge information of the insulator. Based on an attention mechanism, we introduced a structure called attention-block where the backbone extracts the feature map, and this aims to improve the ability of our method to detect insulators. Insulators have a variety of specifications, and the location and granularity of defects are also different. Therefore, we proposed an adaptive threat estimation method based on the area ratio between the entire insulator and the defect area. In addition, in order to solve the problem of data shortage, we established a dataset called InsuDetSet for model training. Experiments on the InsuDetSet dataset demonstrated that our model outperforms existing state-of-the-art models regarding both the detection box and speed.
‘The interface of the future’: Mixed reality, intimate data and imagined temporalities

This article examines discourses about mixed reality as a data-rich sensing technology – specifically, engaging with discourses of time as framed by developers, engineers and in corporate PR and marketing in a range of public facing materials. We focus on four main settings in which mixed reality is imagined to be used, and in which time was a dominant discursive theme – (1) the development of mixed reality by big tech companies, (2) the use of mixed reality for defence, (3) mixed reality as a technology for control of populations in civil society and (4) mixed reality as a technology used in workplace settings. Across these settings, the broad narrative is that mixed reality technologies afford overwhelmingly positive benefits like efficiency and security through their capture, relay and rendition of data (about the environment, about the body etc.) – affording a form of anticipatory power to the user. The framing of temporality, we argue, is underlain by social and political values, which represent certain interests, but leave others out in the imagination of mixed reality's technological advance.
Detailed Mapping of Lava and Ash Deposits at Indonesian Volcanoes by Means of VHR PlanetScope Change Detection

Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis).
Internet-of-Things (IoT) in healthcare and social services – experiences of a sensor system for notifications of deviant behaviours in the home from the users’ perspective

Studies on use of IT in residential care are limited; thus, there is a need for investigations to understand both older people’s and nursing staff’s perspectives on experiences of new technology. ‘Smart homes’ provide home automation solutions, making life easier for those residing there. The aim was to explore, from the users’ perspective, experiences of a sensor system installed in the home. The sensors are meant to provide notifications of deviations in behaviours or routines by the resident, requiring healthcare staff or relatives to do a supervisory visit. The sensor notification system made the users feel secure by being monitored, having control over the situation, and allowing them to become more independent in their daily lives; furthermore, they emphasised the importance of having well-functioning systems. Further development of the technology and use, in co-creation with the users, is needed. Careful preparation in installing/starting the system and repeated information about its aim are needed.
A Survey of Face Recognition based on Convolutional Neural Network

Face recognition is one of the interesting research topics in the field of computer vision. In recent years, deep learning methods, especially the Convolutional Neural Network, have progressed. One of the successes of CNN is in face recognition. Face recognition by computer is a technique done so that the computer can automatically recognize faces in an image. Various researchers have conducted related research on facial recognition. This survey presents researches related to face recognition based on Convolutional Neural Network that has been conducted. The studies used are studies that have been published in the last five years. It was performed to determine the renewal that emerged in face recognition based on Convolutional Neural Network. The basic theory of the Convolutional Neural Network, face recognition, and description of the database used in various researches are also discussed. Hopefully, this survey can provide additional knowledge regarding face recognition based on the Convolutional Neural Network.
Falling Use of Cash and Demand for Retail Central Bank Digital Currency

Cash use in most countries is falling slowly.On the margin, younger adults favor cash substitutes over cash.For older adults it is the reverse.Revealed preference tied to a changing population age structure seems to be the main influence on the demand for cash and why it is falling.Cash use may continue to fall, and card use (the main cash substitute) may fall by more, if CBDC is issued.The extent of this reduction depends on the demand for retail CBDC and the incentives (primarily transaction fees) that can play a determining role in CBDC adoption and use.
BinHOA: Efficient Binary Horse Herd Optimization Method for Feature Selection: Analysis and Validations

In the domains of data mining and machine learning, feature selection (FS) is an essential preprocessing step that has a significant effect on the machine learning model's performance.The primary purpose of FS is to eliminate unnecessary features, resulting in time-space reduction as well as improved the corresponding learning model performance.Horse herd optimization algorithm (HOA) is a new metaheuristic algorithm that mimics the herding behavior of horses.Within a wrapper-based approach, a binary version of HOA is proposed in this study to select the optimal subset of features for classification purposes.The transfer function is the most important aspect of the binary version.Eight transfer functions, S-shaped and V-shaped, are tested to map the continuous search space into binary search space.Two main enhancements are integrated into the standard HOA to strengthen its performance.A Levy flight operator is added to improve the HOA's exploring behavior and alleviate local minimal stagnation.Secondly, a local search algorithm is integrated to enhance the best solution obtained after each iteration of HOA.The purpose of the second enhancement is to increase the exploitation capability by looking for the most promising places discovered by HOA.Large-scaled, middle-scaled, and low-scaled datasets from reputable data repositories are used to validate the performance of the proposed algorithm (BinHOA).Comparative tests with state-ofthe-art algorithms reveal that the Levy flight with the local search algorithm have a significant favorable impact on the performance of HOA.An enhancement of the population diversity is observed with avoidance of being trapped in local optima.
Mobile Robot Path Planning Based on Enhanced Dynamic Window Approach and Improved A<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mi>∗</mi> </msup> </math> Algorithm

Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm with an enhanced dynamic window method is proposed. In the improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments.
Comparing Desktop vs. Mobile Interaction for the Creation of Pervasive Augmented Reality Experiences

This paper presents an evaluation and comparison of interaction methods for the configuration and visualization of pervasive Augmented Reality (AR) experiences using two different platforms: desktop and mobile. AR experiences consist of the enhancement of real-world environments by superimposing additional layers of information, real-time interaction, and accurate 3D registration of virtual and real objects. Pervasive AR extends this concept through experiences that are continuous in space, being aware of and responsive to the user’s context and pose. Currently, the time and technical expertise required to create such applications are the main reasons preventing its widespread use. As such, authoring tools which facilitate the development and configuration of pervasive AR experiences have become progressively more relevant. Their operation often involves the navigation of the real-world scene and the use of the AR equipment itself to add the augmented information within the environment. The proposed experimental tool makes use of 3D scans from physical environments to provide a reconstructed digital replica of such spaces for a desktop-based method, and to enable positional tracking for a mobile-based one. While the desktop platform represents a non-immersive setting, the mobile one provides continuous AR in the physical environment. Both versions can be used to place virtual content and ultimately configure an AR experience. The authoring capabilities of the different platforms were compared by conducting a user study focused on evaluating their usability. Although the AR interface was generally considered more intuitive, the desktop platform shows promise in several aspects, such as remote configuration, lower required effort, and overall better scalability.
Understanding differences between combinations of 2D and 3D input and output devices for 3D data visualization

Focusing on interaction needs for scientific data exploration, we evaluated people’s performance using a 2D mouse, 3D SpaceMouse, or 3D-tangible tablet as input devices to interact with visualizations on 2D screens or stereoscopic augmented reality (AR) head-mounted displays. The increasing availability and power of immersive displays drives us to try to understand how to choose input devices, interaction techniques and output displays for the visualization of scientific data, thus to finally help us guide the interaction design for hybrid AR and PC visualization systems. With a docking task and a clipping plane placement/orientation task, we measure our participants’ performance (completion time and accuracy) with each of the different combinations of input and output. We also report on their perceived workload, their preference, and on other qualitative feedback. Results show that the mouse remains good with any display, especially for tasks that require a high accuracy. Our results highlight the potential to retain the mouse as a primary input device, and to complement it with other 3D interaction devices for specific uses.
Digitalization of Global Society: From the Emerging Social Reality to its Sociological Conceptualisation

The purpose of the article is to consider three important issues from the point of view of synergetic theory: global digitalization of society, digitalization of public administration and sociology of digital society. We consider that the new trend of informatization, which replaced computerization, internetization and networkization, should be recognized as digitalization as the creation of digital network platforms that have analytical and predictive functions. In the process of studying the global digital society, two main questions will be asked: How is it different from the previous stage of information society? What problems of its development await us in the future? The authors reveal the last question with a scenario approach, denoting both a positive and an ambiguous perspective for the development of a digital society. The authors point to the need for the purposeful formation of social institutions in a digital society due to the complexity of the ongoing self-organizing processes. Consideration of the sociology of digital society begins with methodological problems associated with the study of a complex hybrid system due to the unification of real and virtual social spaces, the emergence of techno-subjects and some experience in the use of digital tools in sociology, allowing to work with interactive dynamic data.
Fast and Robust Image Encryption Scheme Based on Quantum Logistic Map and Hyperchaotic System

Topic of quantum chaos has begun to draw increasing attention in recent years. So, to ensure the security of digital image, an image encryption algorithm based on combining a hyperchaotic system and quantum 3D logistic map is proposed. This algorithm is applied in four stages. Initially, the key generator builds upon the foundation of mean for any row or column of the edges of the plain image. Its output value is used to yield initial conditions and parameters of the proposed image encryption scheme. Next, it diffuses the plain image by the random sequences generated by 3D hyperchaotic system, and the diffusion process is realized by implementing XOR operation. Then, the diffused image and chaotic sequences are produced by the 3D quantum chaotic logistic map, expressed as a quantum superposition state using density matrix which is a representation of the state of a quantum system, and finally the resulting quantum image is then confused and diffused simultaneously by a unitary matrix generated by logistic chaos using XNOR operation to obtain the final cipher image. Because of the dependence on the plain image, the algorithm can frustrate the chosen-plaintext and known-plaintext attacks. Simulation results and theoretical analysis verify that the presented scheme has high safety performance, a good encryption effect, and a large key space. The method can effectively resist exhaustive, statistical, and differential attacks. Moreover, the encryption time of the proposed method is satisfactory, and the method can be efficiently used in practice for the secure transmission of image information.
StyleFusion: Disentangling Spatial Segments in StyleGAN-Generated Images

We present StyleFusion , a new mapping architecture for StyleGAN, which takes as input a number of latent codes and fuses them into a single style code. Inserting the resulting style code into a pre-trained StyleGAN generator results in a single harmonized image in which each semantic region is controlled by one of the input latent codes. Effectively, StyleFusion yields a disentangled representation of the image, providing fine-grained control over each region of the generated image. Moreover, to help facilitate global control over the generated image, a special input latent code is incorporated into the fused representation. StyleFusion operates in a hierarchical manner, where each level is tasked with learning to disentangle a pair of image regions (e.g., the car body and wheels). The resulting learned disentanglement allows one to modify both local, fine-grained semantics (e.g., facial features) as well as more global features (e.g., pose and background), providing improved flexibility in the synthesis process. As a natural extension, StyleFusion allows one to perform semantically-aware cross-image mixing of regions that are not necessarily aligned. Finally, we demonstrate how StyleFusion can be paired with existing editing techniques to more faithfully constrain the edit to the user’s region of interest. Code is available at: https://github.com/OmerKafri/StyleFusion .