Reviewerly/exHarmony huggingface dataset:
Generating self-labeled geological datasets for semantic segmentation using pretrained GANs

Recent advancement in deep generative models (GANs) has brought the attention of many researchers to explore the feasibility of using realistic synthetic data as (i) a digital twin of the original dataset and (ii) a new approach to augment the original dataset. Previous works highlighted that GANs can replicate both esthetical and statistical characteristics of datasets, up to the point of being indistinguishable from real samples, even when being examined by domain experts. In addition, the weights learned during the unsupervised training of these generative models are useful to further extract specific features of interest from the given dataset. In geosciences, many computer vision tasks are related to semantic segmentation, from pore quantification to fossil characterization. In such a task, the labeling process becomes the main limiting point, being both time-consuming and requiring domain experts. Hence, in this study, we repurpose GANs to obtain self-labeled geological datasets for semantic segmentation to be readily applicable in geological machine learning workflows. In this work, we used trained style-based GANs of foraminifera specimens, ooids, and mudstones. Our experiments show that with one or a few labels, we can successfully generate self-labeled and synthetic datasets featuring the labels of interest. This achievement is pivotal in geosciences in exploring the idea of GANs for one-shot and few-shot segmentation and in minimizing the efforts of manual labeling for segmentation requiring domain experts.&amp;#160;
Cloud Service Composition and Optimization Selection Based on Hybrid Service Composition Algorithm

In the cloud environment, there are many cloud services with the same function, but there are differences in performance and quality of service (QoS) attributes, so solving service composition and optimization selection (SCOS) in the cloud environment is a complex problem. This study proposes a new method to solve this problem, namely hybrid service composition algorithm, which uses the hunting mechanism of standard ant lion optimization algorithm to find the best service composition path. In this method, on the premise of adapting to the cloud environment and quality of service, crossover and mutation operators are introduced to carry out genetic operation on the population and generate offspring individuals, thus increasing the population size and finding the optimal solution more quickly. In each iteration, the best composite cloud service is selected, and other cloud services are combined according to it. By adjusting genetic factors and iteration times, the population can be better improved and the local optimization of the service composition fitness value can be avoided. In this study, we test and prove the effectiveness of the algorithm for SCOS problems of different sizes. Experimental results show that, compared with GWO algorithm and Ant Lion algorithm, the proposed algorithm has better performance and can find better solutions, and improves accuracy and stability, especially in dealing with large-scale SCOS problems.
Discussion on Network Information Security Based on Cloud Computing Environment

With the explosive advancement of social development and technological change, cloud computing technology has developed rapidly in China in recent years. Data and information security in cloud computing environment has become the focus of attention. Cloud computing technology integrates a large number of computers scattered on the network to build a powerful computer cloud. Computers distributed on this network can read and write data and obtain services according to needs, so as to provide powerful computer cloud platform services. Cloud computing technology is developing rapidly, but there are also many problems. On this cloud platform, how to ensure the security of data stored in the network environment is an urgent problem to be solved. On the one hand, this paper discusses the concept and characteristics of cloud computing technology. On the other hand, it discusses how to ensure data security in the cloud computing environment.
Video Compression Using Convolutional Neural Networks of Video with Chroma Subsampling

In the context of Convolutional Neural Networks based video compression, motivated by the lower acuity of the human visual system for color differences as compared with luma, we investigate a video compression framework using autoencoder networks that encode and decode videos by using less chroma information than luma information. For this purpose, instead of converting Y'CbCr 4:2:2/4:2:0 videos to and from RGB 4:4:4 as per the current state-of-the-art, we have kept the video in Y'CbCr 4:2:2/4:2:0 and merged the luma and chroma channels after the luma is downsampled to match the chroma size. We have performed an inverse function for the decoder. The performance of our models against the 4:4:4 baseline is evaluated by using CPSNR, MS-SSIM, and VMAF metrics. Our experiments reveal that, as compared to video compression involving conversion to and from RGB 4:4:4, the proposed method increases the video quality by about 5% for Y'CbCr 4:2:2 and 6% for Y'CbCr 4:2:0 while reducing the amount of computation by nearly 37% for Y'CbCr 4:2:2 and 40% for Y'CbCr 4:2:0. These results point us to optimization for 4:2:2 and 4:2:0 video of the current state-of-the-art autoencoder.
A New Cloud Computing Deployment Model: Proprietary Cloud

After years of development, cloud computing has developed into public cloud, private cloud, community cloud, hybrid cloud and other cloud forms. However, the government, financial and other industries have more concerns about security, operation and maintenance, cost and other issues in the process of cloud. In this regard, the industry, academia and other parties have launched the frontier exploration, give different solutions. To meet users’ new needs, we proposed a new model that combines the advantages of public and private clouds. It is called proprietary cloud. Proprietary cloud provides users with more efficient management by incorporating years of experience of public cloud providers. On the one hand, it meets the rigid needs of users’ independent control and exclusive resources, on the other hand, it reduces the maintenance cost and downtime probability of users, and achieves the desired effect of users. It mainly includes self-built model and exclusive model deployment modes. The main difference is whether to accept public cloud management. This paper introduces and analyzes proprietary cloud in detail from the aspects of deployment architecture, mode differences and common deployment patterns. It concludes that proprietary cloud is an indispensable cloud computing deployment pattern in the future.
Design and Implementation of Autonomous Navigation System Based on Tracked Mobile Robot

In this paper, we introduce an autonomous exploration and rescue robot system based on a tracked mobile robot platform equipped with a 7 degree-of-freedom (DoF) manipulator, which realizes autonomous navigation in indoor environments and autonomous stair climbing for safe and efficient search and rescue tasks. In Sect. 2, the hardware design of the robot system is presented, which allows flexible movement and high passability to complete obstacle crossing and stair climbing. In Sects. 3 and 4, the indoor navigation algorithm and the stair detection algorithm of the robot system are presented, respectively. The ROS-based system uses the cartographer algorithm for map construction, ROS navigation for autonomous navigation and obstacle avoidance, and a depth camera for stair detection. The process of a four-flipper tracked mobile robot stair climbing is designed. The robot system is experimentally verified in Sect. 5.
Easier Web Navigation Using Intent Classification, Web Scraping and NLP Approaches

The Web has changed significantly in recent years, and websites now carry increasing information which has given rise to websites that can be difficult to navigate because of complex UI. In this work, an approach is presented which will make web navigation easier by incorporating the concepts of data scraping and intent classifiers with a natural language processing model. Our proposed concept is to create a dynamic NLUI (natural language user interface) that makes web navigation easier based on queries that can be text or voice-based. The proposed approach will help the user to obtain answers to their queries which will make web navigation easier. This is done by classifying user queries as intents and the required data is scraped from the website and when integrated with NLP models, helps achieve flexibility in understanding the context to a greater extent. The intent classification model used achieves an accuracy of 99.1 percent on our self-curated dataset. Selenium web drivers and BeautifulSoup have been used for web scraping. The integration of a BERT q/a module was also done, there are endless models and a lot of potential to scale the work and make it more flexible
SketchFashion: Image Translation from Fashion Sketch Based on GAN

Fashion is the embodiment of a cultural symbol and one of the ways we present ourselves to the world. In this paper, we focus on the work process of a fashion designer from drawing fashion sketches to looking for different fabrics to make clothing samples, which usually requires high cost. We propose SketchFashion, which translates the fashion sketches into real images according to the specified style. SketchFashion is implemented through our proposed novel improvements based on SSS2I, and it can generate an image with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$512\times 512$</tex> resolution, which plays an important role in both auxiliary design and apparel e-commerce.
Combining Hard and Soft Constraints in Quantum Constraint-Satisfaction Systems

This work presents a generalization of NchooseK, a constraint satisfaction system designed to target both quantum circuit devices and quantum annealing devices. Previously, NchooseK supported only hard constraints, which made it suitable for expressing problems in NP (e.g., 3-SAT) but not NP-hard problems (e.g., minimum vertex cover). In this paper we show how support for soft constraints can be added to the model and implementation, broadening the classes of problems that can be expressed elegantly in NchooseK without sacrificing portability across different quantum devices. Through a set of examples, we argue that this enhanced version of NchooseK enables problems to be expressed in a more concise, less error-prone manner than if these problems were encoded manually for quantum execution. We include an empirical evaluation of performance, scalability, and fidelity on both a large IBM Q system and a large D- Wave system.
Attributes and Semantic Constrained GAN for Face Sketch-Photo Synthesis

Face sketch-photo synthesis aims at generating a facial photo conditioned on a given photo. It has attracted wide attention in computer vision and has been widely applied in law enforcement and entertainment. However, precisely synthesizing high-quality face photos is still challenging due to the missing color information in face sketches. To alleviate the problem, we propose an attribute and Semantic constrained Generative Adversarial Networks (ASGAN), which introduces face attribute and semantic constraints to improve the quality and accuracy of synthesized photos. Specifically, We first annotate the key attributes in the dataset and form a 14-dimensional attribute vector for each face. Then, the attribute features and semantic features obtained by face parsing are fused. Finally, the generator takes the fused constrain features and sketches as input and constructs two-stream encoders to synthesize high-quality photos. Extensive experimental results demonstrate that our method can significantly outperform state-of-the-art methods. It can synthe-size higher-quality face photos while maintaining the identity, attributes, and structure. Meanwhile, it can alleviate the problem of background and skin color synthesis errors.
A UAV Target Detection Algorithm Based on YOLOv4-Tiny and Improved WBF

UAV target detection is one of the methods to prevent UAV, but the current method has the problems of slow detection speed and poor recognition. In this paper, we propose a UAV target detection algorithm based on YOLOv4-Tiny combining CBAM attention module and improved WBF, which optimizes the detection speed of the algorithm and reduces the problem of missed and false detection of targets. On the self-built UAV target dataset called Drone-dataset, the performance experiment is carried out. Experimental results show that the mAP and FPS of the proposed algorithm reach 91.00% and 129f/s. The parameter of the algorithm is 23.76M, which is only one tenth of YOLOv4. The comprehensive performance of the proposed algorithm is better than SSD, YOLO-Tiny, YOLOv4 and Faster R-CNN, realizing the fast detection of multi-scale UAV targets.
Journalists under attack: self-censorship as an unperceived method for avoiding hostility

This study investigates journalists’ self-censorship and introduces a phenomenon of unperceived collective self-censorship that demands a combination of detection methods. We conducted a content analysis of media critique texts (N=156) that discuss attacks on Estonian journalism. These results were combined with the content analysis of journalistic roles in the news (N=2409) and a survey on journalists (N=99) and completed with semi-structured interviews (N=14). The findings showed that accusations against journalists were frequently related to discourses regarding journalists’ interventionist or watchdog roles. Juxtaposing these results with quantitative data, it became evident that when aspects of interventionist and watchdog roles were criticized in the media texts, the performance of these roles decreased in the news. However, journalists’ self-assessment does not show the perception of this change. We argue that self-censorship was created unknowingly within the newsroom. External pressures – such as politically motivated attacks on journalism – may promote unperceived self-censorship.
TEACHING PRIVACY THROUGH THE DEVELOPMENT AND TESTING OF A LOCATION OBFUSCATION SOLUTION

Day by day, we advance deeper into the universe of technology, of devices and applications that provide services throughout the day for a wide varieties of use scenarios. We discuss solution development and education for developers addressing privacy issues in today's surveillance society. A significant proportion of widely used applications require locating the user. Regarding privacy, there is no effective guarantee that the services will not sell private information to 3rd parties. Users often agree to Terms and Conditions that in practice allow companies to sell data to 3rd parties. There are often instances of data breaches, that overcome legal protection barriers. The need to maintain location privacy is only stronger in today's environment, when context-awareness or location-awareness services are everywhere. Current systems are able to track anyone in real time, with incredible accuracy. We present a solution that provides a way of hiding the exact location, by taking into consideration the user's needs and the most popular areas in a city. The obfuscation range may differ from tens of meters to kilometers. For instance, if people are checking the weather, the location may be set anywhere inside of a city, but if they are looking for restaurants, then it is important to show a nearby obfuscated location. Basic geo indistinguishability solutions are based on Laplace functions. The main drawback is that they are exposed to threads, such as running multiple times the application and getting the distribution formula and the real location. Also, generating fake coordinates without extra checking may lead to wrong positioning, such as over lakes. In order to make the solution reliable, we took into consideration the popular places where someone may be, while avoiding issues related to triangulation. The articles evaluates the state-of-the-art solutions and proposes a new algorithm, which has been tested on a dataset from Beijing. We also discuss how this solution can be used as a case study in teaching privacy for undergraduate students, enlightening critical issues and possible ways of developing alternative solutions that address ethical and technological issues at the same time.
VISUAL FUNCTION TESTING AND TRAINING FOR CHILDREN WITH DIFFERENT VISUAL IMPAIRMENTS, BY USING A SOFTWARE INTERFACE, SERIOUS GAME TYPE FOR LAPTOP AND TABLET

In the paper there is presented an interactive software interface that was designed, programmed, realized and tested in our research activities. It is presented as a serious game, with application for laptop and tablet, to be very attractive for children having visual impairments, aimed for testing and training the visual function in terms of cognitive reaction. The interface it is going to be as attractive as possible for children, which would trigger interest in solving some tests as interesting games. In this way, via software interface implementation, as different game stages, with progressive difficulty degrees, each tested (or trained) child will be stimulated to gather a high score as possible, he could be rewarded according to it. The software interface aims, first of all, to test the visual function for children, by designing images at different levels of contrast, the images being familiar to the children. Giving scores for each stage of testing is automatically determined (running the interface), depending on the subject's response to image perception. For a more proper and complete testing, the interface is presented as three main stages in terms of game, namely for white, for yellow and for green background, these being considered pleasant, relaxing colors for the eye. Until now, the interface (as serious game) was used in case of three children aged between 6 and 9 years, with different visual problems (myopia, squint and amblyopia). It has been found that the software interface, as serious stimulating game, presented a high interest for all three children, and, in case of two of them, the daily resumption of the test (as training for visual function rehabilitation) for two weeks, has resulted in a slight improvement in the visual function. As a result, as further research activities, we intend to test the software interface on several children with sight impairments and, besides, there it is proposed also its subsequent use for activities type visual screening in schools and kindergartens.
Improvements and validation of nextSENSE solar energy nowcasting and short-term forecasting system

Solar energy is one of the main sources of renewable energy nowadays. Since there is a strong dependence of solar power generation on the presence of clouds and aerosols, operational nowcasting and short-term forecasting of solar resources are essential for its integration into the grid. The aim of this study is the assessment of the downwelling surface solar irradiation (DSSI) estimates from the nextSENSE operational service. This service uses as input earth observational data for clouds (EUMETSAT), aerosols (Copernicus Atmosphere Monitoring Service - CAMS) and other important atmospheric parameters to the fast radiative transfer model (RTM) techniques (look-up table &amp;#8211; LUT and multi-parametric equations) in order to derive DSSI in real time over Europe and North Africa in high spatial resolution (5 km at sub-satellite point), every 15 min. Recent modifications relative to the older versions are: (i) the use of multi-parametric equations to obtain the effect of clouds from cloud optical thickness (COT) instead of using Artificial Intelligence techniques, and (ii) the use of more detailed LUT. Forecasted DSSI values are also produced up to 3-hours ahead with a 15-min time step by applying a cloud motion vector (CMV) technique to the COT product based on Meteosat second generation (MSG) satellite data. The new modeled (nowcasted and forecasted) DSSI values were validated against ground-based global horizontal irradiance measurements from pyranometers operating at the Baseline Surface Radiation Network (BSRN) stations and at two additional stations, these of Athens and Thessaloniki, Greece, for the year 2017. The nextSENSE forecasted DSSI values were also benchmarked against the smart-persistence forecast method. The performance of the modeled DSSI values were assessed for different cloud conditions in terms of real cloud modification factor (CMF) values derived by ground-based measurements in conjunction with a clear sky model. Additionally, the effects of aerosol related inputs for estimating DSSI were quantified by comparing the utilized CAMS aerosol optical depth (AOD) forecasts against surface retrievals of the AERONET network. Acknowledgements This study was funded by the European Commission project EuroGEO e-shape (grant agreement No 820852).
Parallel dual-channel multi-label feature selection

In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use single-channel structure to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the concept of dual channel and design the algorithm model as two independent modules. The algorithm obtained different feature correlation sequences, thus avoided relevant feature loss. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective.
Immersive Visualization of Python Coding Using Virtual Reality

The main goal of this study is to develop a mobile Virtual Reality (VR) application to conduct basic Python coding skills for university students who are struggling to learn to code.This study employs a quasi-experimental method to examine the difference in the efficiency of VR and traditional learning methods by evaluating the students' performance.Thirty students between 18 to 22 years old participated.The participants were divided into two control groups, in which one group used the conventional python learning method while another implemented the VR application in python learning.Unity 3D was used as the application development tool with Mobile Application Development Lifecycle (MADLC).The developed VR application was employed using Google cardboard to create an immersive VR experience.Usability tests, hypothesis tests, Presence Questionnaires (PQ) and system usability scale (SUS) are used as evaluation tools.Findings illustrated how learning through VR has yielded better performance than the conventional learning method.In hypothesis testing, the VR learning method suggested more effective learning with t_statistic value of 4.992, a more considerable value than t_critical=2.76.73% of the participants rated above 68 out of 100, which indicated high levels of satisfaction with the use of the mobile VR application to learn Python.In short, the VR method is perceived to be useful and convenient to help students learn at any place and time.