Reviewerly/exHarmony huggingface dataset:
Efficient Monocular Depth Estimation for Edge Computing Platforms

Estimating depth is necessary to understand and navigate the environment surrounding us. Over the years, many active sensors have been developed to measure depth, but they are expensive and require additional space for mounting. A cheaper alternative is estimating depth from a single RGB image taken by an ordinary monocular camera, which can be placed inside the smartphone. However, state-of-the-art depth estimation algorithms are based on complex deep neural networks that are too slow for real-time inference on mobile platforms which can be mounted, for instance, on a micro aerial vehicle. That fact is a barrier to the further development of monocular depth estimation. In this paper, we address this problem. We utilize recent advancements in the architecture of lightweight networks to reduce complexity. We propose a novel lightweight network design with competitive accuracy and significant complexity reduction compared to existing approaches. Our methodology indicates that it is possible to achieve inference speeds accelerated by an order of magnitude on NVIDIA Jetson Nano and, at the same time, preserve the comparable accuracy on the KITTI Odometry dataset in comparison with the current state-of-the-art algorithms.
Blockchain Suitability: A Decision-Making Flowchart and Web

Blockchain technology holds immense potential across various industries, with novel applications emerging continuously. However, its unique features and inherent complexity necessitate the development of a robust decision-making frame-work to assess its suitability for specific applications. This paper contributes to the discourse by proposing a comprehensive decision flowchart for evaluating the appropriateness of blockchain technology across various use cases. Our work enhances previous frameworks by integrating new insights from recent research and practical implementations, notably the focus on the potential for blockchain technology to enhance data security and integrity in single private organizations. In particular, our decision flowchart incorporates these use cases, providing a more nuanced and adaptable tool for assessing blockchain suitability. Further, we present the implementation of this model through an interactive web interface to facilitate ease of use and accessibility. This work will serve as a valuable resource for IT professionals, blockchain developers, and firms seeking to harness the potential of blockchain technology.
Background Extraction from Image using Depth Estimation

This article focuses on creating a 3D depth representation and successfully extracting the background based on the acquired results. Furthermore, this work deals with studying the current state of the art and exploring the possibilities of their practical application. The objective was also to study the issues related to background removal or modification in images and provide an overview of available methods for this particular application. The main goal was to design and implement an algorithm for background extraction. The proposed algorithm was then compared with another existing method, that solves the mentioned problem. The conclusion consists of evaluating the results of each method and describing their strengths and weaknesses.
Simple and Reliable Encryption of Color Images

This article proposes a method for simple and reliable encryption of digital images using chaotic transformations of Arnold's cat and Baker maps. The transformed source image is then combined using the XOR operation with a random image generated by a simple sensor of a pseudo-random sequence of numbers distributed according to a uniform distribution law. The study conducted by the authors shows the impossibility of statistical attacks on an encrypted image by intruders due to its high cryptographic strength.
Adaptive Camera System for Enhanced Virtual Teleportation: Expanding the Boundaries of Immersive Experiences

This research article presents the utilization of an adaptive camera system for virtual teleport in videoconferencing, enabling users to experience remote locations in an immersive and interactive manner. The camera system incorporates features such as motion of camera system in X and Y axes, object tracking, and detection using Ultralytics YOLO V5 (You Only Look Once). The live video feed from the camera system is streamed over the internet by using OBS (Open Broadcaster Software version 29.1.3) virtual camera via platforms like Google Meet and viewed through VR headsets. Users can control the camera's movement and direction using intuitive interfaces like joysticks, enhancing their sense of presence and immersion. This article highlights the design and implementation of the adaptive camera system, its integration with virtual reality technologies, and the user experience it offers. The findings demonstrate the potential of this system for providing a realistic and engaging virtual teleportation experience in future.
Body Part Classification from Medical Single Channel Images Using Deep Neural Networks

In general image recognition is aimed for classification of objects in RGB images and photos. However, many medical devices produce single channel images such as X-ray and MRI. There are several successful architectures with pre-trained input layers which are used for many classification tasks. In this paper ResNet with several configurations and one custom neural network architecture were used to classify human body parts in medical single channel images. From testing results it can be concluded that designed network provided better results than pre-trained ResNet. Medical MNIST dataset was used for the testing. Achieved results can be used in development of intelligent medical databases with AI filtering and processing of images.
Comparative Analysis of Neural Network and Dynamic Time Warping in Online Signature Verification

In this paper we explore the possibilities of using Neural Networks with transfer learning approach and Dynamic Time Warping for online signature verification. Verification process of proposed methods is based on SVC-2004 signature database, which contains genuine and skilled forgery signatures. Experimental results show that approach using Convolutional Neural Networks is more successful and this method is achieving an Equal Error Rate (EER) as low as 2.25% using 10-fold cross-validation. These results are competitive with other published works.
Quality Analysis of Coded Video Stream in Distributed Computing System

The aim of the paper is the quality of video streaming analysis in cases of using different video codecs in the environment of distributed computer systems with different QoS (Quality of Service). For the purposes of the analysis, several scenarios were set up in which video encoded with different codecs is transmitted by a virtual video streaming server to virtual clients. For each of the scenarios, an environment with different QoS (packet losses, latency, jitter) was simulated and the quality of the received video stream was evaluated for each video codec. The quality of the received decoded video stream was calculated using SSIM (Structural Similarity) and VMAF (Video Multimethod Assessment Fusion) video objective metrics and compared to the original video stream.
Prediction of Solar Energy from Sky Images and Meteorological Data Using Neural Networks

Many of the renewable energy generation methods suffer from the variable power output they produce. This is because they depend on elements with a substantial stochastic nature. To mitigate the variability different controlling mechanisms must be deployed. For thorough and optimal control, it is a necessity to predict the generated power in the near or more distant future. In this article, we explore the possibility of predicting electricity production by photovoltaic panels based on sky images. In addition, we also tested selected meteorological data that are relevant and available. For this purpose, we used SkyCam dataset that contains required information. We have proposed 3 scenarios combining block-based and sequential processing of sky images and selected meteorological data using CNN and LSTM networks. The best results were obtained by a combination of sky images and meteorological data processed as sequence of frames by LSTM. The achieved mean absolute error was 44.3 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for 15-minute intervals.
LPNet: A Reaction-Based Local Planner for Autonomous Collision Avoidance Using Imitation Learning

In this work, we propose a reaction-based local planner for autonomous collision avoidance of quadrotor in obstacle-cluttered environment without relying on an explicit map. Our approach searches for feasible trajectory using a set of motion primitives in state lattice and represents the optimal one as a polynomial by solving an optimal control problem. A modified Q-network, termed LPNet, is presented to predict the action-values of motion primitives from the current depth image and the state estimation of the quadrotor directly. To train the proposed LPNet, a primitive-based expert policy with privileged information about the surroundings and unconstrained computational budget is developed to provide demonstrations for imitation learning. Finally, a series of experiments are conducted to demonstrate the effectiveness and time-efficiency of the proposed method in both simulation and real-world.
Permission-Based Blockchain Consortium Network (PBCN) uses for Cross-Border Payment

The introduction of a permission-based blockchain consortium network for cross-border payment systems has completely changed how businesses run and made it possible for trade and commerce to take place on a worldwide scale.However, most of the current cross-border payment systems are only operational in pilot mode, which presents several difficulties such as high transaction fees and a drawn-out settlement procedure, as well as another crucial issue called trust in the payment gateway system.Blockchain technology has arisen in recent years as a potential answer to these problems, including quicker transaction speeds, cheaper transaction costs, and better security.In this study, a unique database storage management system and transaction processing mechanism have been used to propose a novel permission-based consortium blockchain network for cross-border payment systems, particularly for banking and finance.
Sequential stochastic and bootstrap methods to generate synthetic solar irradiance time series of high temporal resolution based on historical observations

Generating synthetic solar irradiance data through computational methods has become an attractive approach, especially when obtaining accurate measurements from historical observations or satellite-derived estimates is not feasible. Since solar photovoltaic energy production is influenced by variable weather conditions, having reliable solar irradiance data is crucial for evaluating the performance of photovoltaic systems accurately. In this paper, we propose two methods, namely stochastic method, and bootstrap method, to generate one-day synthetic solar irradiance data at a minimum 60-minute time resolution. Both techniques consider dynamic meteorological behavior and maintain the physical significance of the observed data, effectively capturing the intermittent nature of solar irradiance. Validations were performed for five sites with different climate conditions and significant geographical separation, and different time resolutions, through various metrics, including variability, statistical distribution, and energy production, which demonstrated mean average percentage errors as low as 2.1% for intraday irradiance fluctuations, statistical distribution goodness-of-fit up to 80%, and discrepancy for energy production close to 2.4%. results verify the applicability of the proposed methods regardless the time resolution, the location of the measured data and the procedure to record it (i.e., from ground weather stations or satellite-derived estimates). We also evaluated the computational performance of the proposed methods, synthetic, where the elapsed time to generate the synthetic data takes less than a minute to generate a hundred one-day solar irradiance time series. The novelty of the proposed methods is that it is only needed the information of a single month to generate representative synthetic solar irradiance one-day sequences generated for five climate conditions without the need of a training process (i.e., iterative adjustments of the parameters), being useful for pre-feasibility studies of the photovoltaic project and to train machine learning algorithms that deals with time series. To encourage the reproducibility of the research presented in this paper, the proposed methods to generate synthetic solar irradiance data are freely available in a well-documented GitHub repository from https://github.com/salazarna/synthetic-irradiance-sequence.
Education in Mexico and technological public policy for developing complex thinking in the digital era: A model for technology management

Currently, numerous studies highlight the importance of technology as an effective resource for improving the quality of education. Like other countries in the region, Mexico greatly emphasizes improving its education system comprehensively and competitively, and technology has become a fundamental strategy to achieve this goal. Technology has allowed it to fill existing gaps and introduce innovative models in the digital age, promoting human capital development and fostering thriving communities. Technology also facilitates the development of competencies, such as complex thinking and the skills required to utilize Education 4.0 resources. However, these advances are at risk without examining the possibilities for technology transfer in the region's educational institutions. Accordingly, the following questions arise: a) What is the probability of achieving technology transfer to primary education institutions; b) What investments or modifications are necessary in the existing infrastructure to transfer educational and other technologies? We aimed to develop a quantitative data analysis model to examine the capacity of primary education institutions, applicable also to higher education, to adapt, assimilate, and transfer technology to improve educational quality in Mexico, as proposed by González Sabater in 2011. The results suggest the feasibility of a technology management system validated through a reliable database, considering the existing infrastructure as a basis for technology transfer. This research is relevant in establishing the probability of success of an educational institution in completing the process of assimilation and technology transfer, thus also contributing to governmental decision-making on educational spending.
Validation of a closed-loop aim point management system at the Jülich solar tower

Due to modeling errors and disturbances caused by clouds, Solar Power Tower (SPT) plants consider high safety margins during operation leading to a significant loss of revenues. This conservatism contradicts the expansion of SPT plants despite their potential. In this work, an aim point management system is deployed at the open-volumetric receiver of the Jülich solar tower. It employs All Sky Imager (ASI)-based nowcasts as well as flux density and temperature measurements. While the temperature measurements provide feedback for an outer receiver control loop, the flux density measurements offer feedback for an inner heliostat field control loop of a cascade control. The receiver controller estimates flux density setpoints for the inner control loop. By comparing these flux density setpoints with measured flux densities, a static optimal control technique compensates for modeling errors and increases the intercept. This control requires a fast aim point optimization, the enhanced ant-colony optimization meta-heuristic. Furthermore, a feed forward controller compensates for disturbances due to clouds by scaling the heliostats' reflected power used in the system model by DNI nowcasts. At the Jülich solar tower, the static optimal control compensates for changes in the allowable flux density in less than three control steps. The cascade control controls the subreceivers in a narrow temperature range of 15 K, which has never been achieved before at this tower.
Online Simulator-Based Experimental Design for Cognitive Model Selection

Abstract The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discriminate between theories expressed as computational models. Research on this subject has mostly been restricted to optimal experiment design with analytically tractable models. However, cognitive models of increasing complexity with intractable likelihoods are becoming more commonplace. In this paper, we propose BOSMOS, an approach to experimental design that can select between computational models without tractable likelihoods. It does so in a data-efficient manner by sequentially and adaptively generating informative experiments. In contrast to previous approaches, we introduce a novel simulator-based utility objective for design selection and a new approximation of the model likelihood for model selection. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to two orders of magnitude less time than existing LFI alternatives for three cognitive science tasks: memory retention, sequential signal detection, and risky choice.
Mapping Molecular Hamiltonians into Hamiltonians of Modular cQED Processors

We introduce a general method based on the operators of the Dyson-Masleev transformation to map the Hamiltonian of an arbitrary model system into the Hamiltonian of a circuit Quantum Electrodynamics (cQED) processor. Furthermore, we introduce a modular approach to programming a cQED processor with components corresponding to the mapping Hamiltonian. The method is illustrated as applied to quantum dynamics simulations of the Fenna-Matthews-Olson (FMO) complex and the spin-boson model of charge transfer. Beyond applications to molecular Hamiltonians, the mapping provides a general approach to implement any unitary operator in terms of a sequence of unitary transformations corresponding to powers of creation and annihilation operators of a single bosonic mode in a cQED processor.
Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Abstract Remote sensing geochemistry is a simple, fast and economical advanced prospecting method, which carries out inversion and prediction of surface element content using the empirical model by regression or machine learning. The key problem faced by quantitative remote sensing is the low inversion accuracy of the model due to the mismatch of “point surface” information. How to overcome this problem? This paper proposes a “surface to surface” modeling method, which converts point data into surface data through Kriging interpolation to solve this problem. This paper uses geochemical interpolation data of Cu elements at different scales in the Qishitan gold mine area, Xinjiang, and ASTER remote sensing data to conduct geochemical modeling. In order to test the effect of Kriging on decreasing the scale effect, five sets of experiments were designed for comparison. The first four sets of sample data were interpolated according to different cell sizes, and the last set of data was not interpolated. The results show that the Kriging interpolation based on the ground resolution of the remote sensing image can effectively improve the accuracy of the remote sensing quantitative inversion model. When the square interpolation is close to the ground resolution of the used remote sensing data, the modeling accuracy gets the best value. This paper provides a new idea for improving the accuracy of remote sensing geochemical modeling.
Flow-models 2.0: Elephant flows modeling and detection with machine learning

This article presents the new version of the flow-models IP network flow modeling framework. The improved features include flow skipping and counting, flow filtering, IP address anonymization, and time series data calculation. The new version also enables simulation of the first packet mirroring feature and provides tools for modeling the detection of elephant flows. It includes examples of using the scikit-learn library to build machine learning models for elephant flow detection based on the first packet. Furthermore, it provides an anonymized flow dataset, enabling researchers to train and validate machine learning models for traffic analysis in a reproducible and comparable manner.
GaitTracker: A Digital Platform for Measuring, Detecting and Analyzing Gait Changes

Gait is an essential bio-marker for long-term health. Traditionally, Gait analysis depends on vision-based or expensive pressure mats where patients are instructed to walk or perform standard limb movements. Early monitoring and detection of gait changes could prevent severe conditions. However, such tests happen late in the onset of the problem. Despite a plethora of wearable devices, such as fitness bands and health trackers, no single device monitors gait and provides an early diagnosis. This work presents a proof-of-concept of our in-house developed wearable inertial measurement unit (IMU) for extracting gait patterns. In addition, the results presented in this work detect changes in gait patterns. The device was tested with ten volunteers (six males and four females, 25 +/− 1.8 years, 163 +/− 8.8 cm) who provided data for both normal and abnormal walking resulting in around 700 gait samples. The results show that sudden changes in gait can be detected with an affordable and portable wearable device.
Policy advisory bodies in Korea

This chapter introduces policy advisory bodies in South Korea. In South Korea, policy advisory councils in the central government serve the function of providing professional opinions and assisting in decision making through deliberation, mediation and consultation. This chapter provides general overviews of the councils, followed by a review of their operational records to assess their activities and influence by analysing news coverage and number of meetings held. Three example cases are selected and used to illustrate the impacts and limitations of policy advisory bodies. Recommendations to enhance the influence and effectiveness of councils are also discussed alongside ideas for further research in this area.
The Ethics of Hacking

Political hackers, like the famous Anonymous collective, have demonstrated their willingness to use their hacking skills and political violence to further their agendas. This can look like vigilantism, as they carry out their own private political beliefs. However, many of their causes – targeting terrorist groups, fighting for LGBTQ+ rights, and protecting people’s freedom of expression, autonomy and privacy – are intuitively good things to fight for. This book creates a new framework that argues that when the state fails to protect people, hackers can intervene, and evaluates hacking based on the political or social circumstances. It highlights the space for hackers to operate as legitimate actors; guides hacker activity by detailing what actions are justified toward what end; outlines mechanisms to aid hackers in reaching ethically justified decisions; and directs the political community on how to react to these political hackers. Applying this framework to the most pivotal hacking operations within the last two decades, including the Arab Spring, police brutality in the USA and the Nigerian and Ugandan governments’ announcements of homophobic legislation, it offers a unique contribution to conceptualizing hacking as a contemporary political activity