Reviewerly/exHarmony huggingface dataset:
A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

Thresholding is a method for region-based image segmentation, which is important in image processing applications such as object recognition Multilevel. Thresholding is used to find multiple threshold values. Image segmentation plays a significant role in image analysis and pattern recognition. While threshold techniques traditionally are quite well for bi-level thresholding algorithms, multilevel thresholding for color images may have too much processing complexity. Swarm intelligence methods are frequently employed to minimize the complexity of constrained optimization problems applicable to multilevel thresholding and segmentation of color (RGB) images; In this paper, the hybrid Jaya algorithm with the SA algorithm was proposed to solve the problem of computational complexity in multilevel thresholding. This work uses Otsu method, Kapur entropy and Tsallis method as techniques to find optimal values of thresholds at different levels of color images as the target Tasks Experiments were performed on 5 standardized color images and 3 grayscale images as far as optimal threshold values are concerned, Statistical methods were used to measure the performance of the threshold methods and to select the better threshold, namely, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), FSIM (Feature Similarity Index) and values of objective at many levels. The experimental results indicate that the presenter's Jaya and Simulated Annealing (JSA) method is better than other methods for segmenting color (RGB) images with multiple threshold levels. On the other hand, the Tsallis entropy of the cascade was found to be more robust and accurate in segmenting color images at multiple levels.
The Impact of the COVID-19 Pandemic on attitudes towards Human-Robot Collaboration in the Healthcare Environment

The need of nursing staff will continue to increase in the next years. At the same time, the healthcare system will face an increase of elderly people. So, the future society will be confronted with socio-political and economic challenges. It is expected that human-robot collaboration will increase in healthcare. This should support and relieve care staff. An online study was conducted to examine differences in the acceptance of different care robots and the general attitude towards robots in the healthcare sector. To additionally analyze the impact of the COVID-19 pandemic, two different measurement times were used, one at the beginning of the pandemic resp. after the first corona wave and the first lockdowns, and one five months later. The results of the study should help to understand the acceptance of the potential use of different robots in healthcare and the impact of the COVID-19 pandemic.
CONVERGENCE OF EDUCATION AND SELF-EDUCATION: CONTRADICTIONS IN THE THEORY AND PRACTICE OF TRAINING PUBLIC ADMINISTRATION SPECIALISTS

The article is devoted to the problem of self-education competence formation in the process of basic educational programs realization in the higher school and its further development in the process of professional activity realization in the sphere of public administration. Historical and modern interpretations of the concept "self-education" are considered. Particular attention is paid to the problems of internal and external motivation of students to self-education. The necessity of convergence of educational process and self-education, which requires a special scientific and methodological support, is substantiated. The accumulated global changes in all spheres of life, including professional activity, and the increasing dynamism of these changes require a fundamental modernization of the educational process, the use of mixed pedagogical technologies and the use of digital educational resources. Their focus on the formation of the ability to self-educate creates an opportunity for students to advance education and professional mobility in a changing world. This becomes an existential competence of a public civil servant and characterizes the level of his managerial maturity.
Complex Physics with Graph Networks for Industrial Material Flow Simulation

Material flow simulation is essential to modern manufacturing engineering. Physically based, discrete material flow simulation is slow, and, because it is not well suited for parallelization, it does not benefit from vertical scaling of the computer infrastructure. Other methods, like based on hyperbolic partial differential equations or Navier-Stokes based continuous simulations can be significantly faster, however switching between continuous and discrete representations is not yet solved. Machine learning methods have been used recently to speed up physically based simulations. In this paper we show, that the latest advances in machine learning solutions using graph networks can be applied to speed up industrial material flow simulations while still using discrete representations for the material flow objects.
The Experience of Immersive Virtual Reality: A Phenomenology Inspired Inquiry

Immersive Virtual Reality (IVR) technology is becoming central for Information Systems (IS) research. However, existing studies in IS fall short of providing insights into how the IVR experience becomes meaningful for end-users. To increase granularity and specificity in this regard, researchers have suggested that the IVR experience can become meaningful due to its fleeting feeling of escapism. In this paper, I explore and characterize how individuals use the IVR experience to create meaning in the context of meaningful escapism, by undertaking a phenomenology inspired inquiry, based on Heideggerian views on meaning, meaningfulness, and world. Interviews and analysis were conducted within an empirical case of IVR fire safety training. As a result, four characteristics of the IVR experience as a meaningful form of escapism were unveiled: a sense of content, a sense of familiarity, a sense of mood, and a sense of care. Throughout this study, I offer a nuanced perspective on how the characteristics contribute to clarifying the distinctions and relationship between meaning and meaningfulness, as well as how the IVR experience becomes a meaningful escapism that provides an alternative of individual’s being-in-the-world, into a being-in-the-virtual-world, also known as Virtual Dasein. Further, this study contributes to the IS field by advancing the current discourse on IVR research and escapism, from a phenomenological perspective.
A Reconfigurable Linear RF Analog Processor for Realizing Microwave Artificial Neural Network

Owing to the data explosion and rapid development of artificial intelligence (AI), particularly deep neural networks (DNNs), the ever-increasing demand for large-scale matrix-vector multiplication has become one of the major issues in machine learning (ML). Training and evaluating such neural networks rely on heavy computational resources, resulting in significant system latency and power consumption. To overcome these issues, analog computing using optical interferometric-based linear processors have recently appeared as promising candidates in accelerating matrix-vector multiplication and lowering power consumption. On the other hand, radio frequency (RF) electromagnetic waves can also exhibit similar advantages as the optical counterpart by performing analog computation at light speed with lower power. Furthermore, RF devices have extra benefits such as lower cost, mature fabrication, and analog-digital mixed design simplicity, which has great potential in realizing affordable, scalable, low latency, low power, near-sensor radio frequency neural network (RFNN) that may greatly enrich RF signal processing capability. In this work, we propose a 2X2 reconfigurable linear RF analog processor in theory and experiment, which can be applied as a matrix multiplier in an artificial neural network (ANN). The proposed device can be utilized to realize a 2X2 simple RFNN for data classification. An 8X8 linear analog processor formed by 28 RFNN devices are also applied in a 4-layer ANN for Modified National Institute of Standards and Technology (MNIST) dataset classification.
On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach

In recent years, word embeddings have been widely used to measure biases in texts. Even if they have proven to be effective in detecting a wide variety of biases, metrics based on word embeddings lack transparency and interpretability. We analyze an alternative PMI-based metric to quantify biases in texts. It can be expressed as a function of conditional probabilities, which provides a simple interpretation in terms of word co-occurrences. We also prove that it can be approximated by an odds ratio, which allows estimating confidence intervals and statistical significance of textual biases. This approach produces similar results to metrics based on word embeddings when capturing gender gaps of the real world embedded in large corpora.
From Dragondoom to Dragonstar: Side-channel Attacks and Formally Verified Implementation of WPA3 Dragonfly Handshake

It is universally acknowledged that Wi-Fi communications are important to secure. Thus, the Wi-Fi Alliance published WPA3 in 2018 with a distinctive security feature: it leverages a Password-Authenticated Key Exchange (PAKE) protocol to protect users' passwords from offline dictionary attacks. Unfortunately, soon after its release, several attacks were reported against its implementations, in response to which the protocol was updated in a best-effort manner. In this paper, we show that the proposed mitigations are not enough, especially for a complex protocol to implement even for savvy developers. Indeed, we present **Dragondoom**, a collection of side-channel vulnerabilities of varying strength allowing attackers to recover users' passwords in widely deployed Wi-Fi daemons, such as hostap in its default settings. Our findings target both password conversion methods, namely the default probabilistic hunting-and-pecking and its newly standardized deterministic alternative based on SSWU. We successfully exploit our leakage in practice through microarchitectural mechanisms, and overcome the limited spatial resolution of Flush+Reload. Our attacks outperform previous works in terms of required measurements. Then, driven by the need to end the spiral of patch-and-hack in Dragonfly implementations, we propose **Dragonstar**, an implementation of Dragonfly leveraging a formally verified implementation of the underlying mathematical operations, thereby removing all the related leakage vector. Our implementation relies on HACL*, a formally verified crypto library guaranteeing secret-independence. We design Dragonstar, so that its integration within hostap requires minimal modifications to the existing project. Our experiments show that the performance of HACL*-based hostap is comparable to OpenSSL-based, implying that Dragonstar is both efficient and proved to be leakage-free.
A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing

Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.
Data Science: Trends and Its Role in Various Fields

Data Science is a field that has developed rapidly in recent years, which utilizes technology and data analysis methods to produce useful information for various fields. In this paper, we will explain the latest trends in the field of Data Science and their role in various fields, including the fields of business, health, finance, and the public sector and discuss the technologies and algorithms used in Data Science, such as Machine Learning, Natural language Processing., data visualization, Big Data Analytics, and provides examples of how this technology can be used to solve problems in various fields. This paper aims to explore that Data Science has a very important role in various fields, and will continue to develop along with technological developments. The literature study methodology was carried out to collect data and information related to this topic, which aims to ensure that the data analysis carried out is accurate, and can be trusted. The results of the study found that Data Science science and technology has helped a number of parties with its application to certain aspects related to their fields. Thus, this paper can be a useful source of information for professionals and researchers who are interested in Data Science and its role in various fields.
amIcompositional: Simple Tests for Compositional Behaviour of High Throughput Data with Common Transformations

Compositional approaches are beginning to permeate high throughput biomedical sciences in the areas of microbiome, genomics, transcriptomics and proteomics. Yet non-compositional approaches are still commonly observed. Non-compositional approaches are particularly problematic in network analysis based on correlation, ordination and exploratory data analysis based on distance, and differential abundance analysis based on normalization. Here we describe the aIc R package, a simple tool that answers the fundamental question: does the dataset or normalization exhibit compositional artefacts that will skew interpretations when analyzing high throughput biomedical data? The aIc R package includes options for several of the most widely used normalizations and filtering methods. The R package includes tests for subcompositional dominance and coherence along with perturbation and scale invariance. Exploratory analysis is facilitated by an R Shiny app that makes the process simple for those not wishing to use an R console. This simple approach will allow research groups to acknowledge and account for potential artefacts in data analysis resulting in more robust and reliable inferences.
Face Detection and Recognition Using Raspberry PI Computer

This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this paper demonstrates use of a Raspberry Pi computer, along with supplementary tools and software, to detect and recognize faces using a pre-defined dataset.
Radio frequency fingerprint-based drone identification and classification using Mel spectrograms and pre-trained YAMNet neural

The convergence of drones with the Internet of Things (IoT) has paved the way for the Internet of Drones (IoD), an interconnected network of drones, ground control systems, and cloud infrastructure that enables enhanced connectivity, data exchange, and autonomous operations. The integration of drones with the IoD has opened up new possibilities for efficient and intelligent aerial operations, facilitating advancements in sectors such as logistics, agriculture, surveillance, and emergency response This paper introduces a novel approach for drone identification and classification by extracting radio frequency (RF) fingerprints and utilizing Mel spectrograms as distinctive patterns. The proposed approach converts RF signals to audio signals and leverages Mel spectrograms as essential features to train neural networks. The YAMNet neural network is employed, utilizing transfer learning techniques, to train the dataset and classify multiple drone models. The initial classification layer achieves an impressive accuracy of 99.6% in distinguishing between drones and non-drones. In the subsequent layer, the model achieves 96.9% accuracy in identifying drone types from three classes, including AR Drone, Bebop Drone, and Phantom Drone. At the third classification layer, the accuracy ranges between 96% and 97% for identifying the specific mode of each drone type. This research showcases the efficacy of Mel spectrogram-based RF fingerprints and demonstrates the potential for accurate drone identification and classification using pre-trained YAMNet neural networks.
IMPLEMENTASI METODE TSUKAMOTO UNTUK SISTEM PEMILIHAN MAKANAN SEHAT BAGI IBU HAMIL

Healthy Food Selection System for Pregnant Women Is a web-based system designed to help pregnant women choose healthy foods during pregnancy (Trimester 1-Trimester 3) based on the number of calories needed in one day by taking into account the nutritional adequacy rate table. The food selection system is designed with ` =the Tsukamoto fuzzy logic method with the hope that the system can produce food recommendations based on the number of calories close to the ideal standard of the Nutritional Adequacy Ratio (RDA). In order To determine the level of accuracy of the system that has been made, validation of the ideal data on the Nutrition Adequacy Rate (RDA) is carried out with a total of 20 pregnant women. The results of the validation of the data generated by the system with ideal data obtained very close results with the largest error rate of 24.89%, so the conclusion is that the healthy food selection system by applying the Tsukamoto fuzzy logic method can be used to recommend healthy foods for pregnant women.
Hazardous Echoes: The DNS Resolvers that Should Be Put on Mute

Connectionless networking protocols such as DNS continue to be widely misused for Reflection & Amplification (R&A) DDoS attacks. Early efforts to address the main cause of DNS-based R&A were focused on identifying and attempting to eradicate open DNS resolvers. One characteristic of open resolvers that has not received much attention so far is that – as a result of unexpected behavior – resolvers can react to a single query with multiple DNS responses. We refer to these as Echoing Resolvers. In this paper, we quantify the problem of echoing resolvers in the wild. We identify thousands of such resolvers on the Internet and show how some reply on the order of tens of thousands of times to a single query, further escalating the potential of R&A DDoS attacks. We analyze the cause of response repetition, study behavioral differences among echoing resolvers, and categorize resolvers on the basis of the underlying causes of the observed behavior. We show how the interplay between DNS traffic and the traversed networks is responsible for echoing resolvers. In particular, we identify IP broadcasting as a cause of echoing resolvers, on top of phenomena already described in the literature (e.g., routing loops). Furthermore, we show that using sensitive labels in queries can lead to a more powerful echoing effect while using different query types does not significantly affect echoing behavior. Finally, seeing how some underlying causes of response repetition also affect or can be turned against authoritative nameservers, we quantify the potential impact of echoing resolvers on these as well.
MEMS mirror based eye tracking: simulation of the system parameter effect on the accuracy of pupil position estimation

Eye tracking systems are mostly video-based methods which require significant computation to achieve good accuracy.An alternative method with comparable accuracy but less computational expense is 2D microelectromechanical (MEMS) mirror scanning.However, this technology is relatively new and there are not many publications on it.The purpose of this study was to examine how individual parameters of system components can affect the accuracy of pupil position estimation.The study was conducted based on a virtual simulator.It was shown that the optimal detector field of view (FOV) depends on the frequency ratio of the MEMS mirror axis.For a value of 1:13, the smallest errors were at 0.1 • , 1.65 • , 2.3 • , and 2.95 • .The error for the impact of the signal sampling rate above 3 kHz stabilizes at 0.065 • and no longer changes its value regardless of increasing the number of samples.The error for the frequency ratio of the MEMS mirror axis increases linearly in the range of 0.065 • -0.1 • up to the ratio of 1:230.Above this there is a sudden increase to the average value of 0.3 • .The conducted research provides guidance in the selection of parameters for the construction of eye tracking MEMS mirror-based systems.
Hardware-Conscious Optimization of the Quantum Toffoli Gate

While quantum computing holds great potential in combinatorial optimization, electronic structure calculation, and number theory, the current era of quantum computing is limited by noisy hardware. Many quantum compilation approaches can mitigate the effects of imperfect hardware by optimizing quantum circuits for objectives such as critical path length. Few approaches consider quantum circuits in terms of the set of vendor-calibrated operations (i.e., native gates) available on target hardware. This manuscript expands the analytical and numerical approaches for optimizing quantum circuits at this abstraction level. We present a procedure for combining the strengths of analytical native gate-level optimization with numerical optimization. Although we focus on optimizing Toffoli gates on the IBMQ native gate set, the methods presented are generalizable to any gate and superconducting qubit architecture. Our optimized Toffoli gate implementation demonstrates an 18% reduction in infidelity compared with the canonical implementation as benchmarked on IBM Jakarta with quantum process tomography. Assuming the inclusion of multi-qubit cross-resonance (MCR) gates in the IBMQ native gate set, we produce Toffoli implementations with only six multi-qubit gates, a 25% reduction from the canonical eight multi-qubit implementations for linearly connected qubits.