Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence in Modern Ecology for Detecting Plant Pests and Animal Diseases

Climate change could lead to an increase in diseases in plants and animals. Plant pathogens have caused devastating production losses, such as in tropical countries. The development of algorithms that match the accuracy of plant and animal disease detection in predicting the toxicity of substances has continued through a massive database. Data and information from 10,000 substances from more than 800,000 animal tests have been carried out to generate the algorithms. Plant and animal disease detection using artificial intelligent in the modern ecological era is important and needed. Diseases in animals are still found in several Ruminant-Slaughterhouses. The purpose of the study is to identify the leverage attributes for using of Artificial Intelligent (AI) in detecting plant pests and animal diseases. The use of Multidimensional Scaling (MDS) produces a leverage attribute for the use of AI in detecting plant pests and animal diseases. The results showed that leverage attributes found were: Prediction of the presence of proteins structures produced by pathogens with a Root Mean Square (RMS) value of 4.5123; and Plant and Animal Disease Data will be opened with an RMS value of 4.2555. The findings of this study in the real world are to produce the development of smart agricultural applications in detecting plant pests and animal diseases as an early warning system. In addition, the application is also useful for eco-tourism managers who have a natural close relationship with plants and animals, so that ecological security in the modern ecological era, can be better maintained.
Sequential phonon measurements of atomic motion

Abstract The motion of trapped atoms plays an essential role in quantum mechanical sensing, simulations and computing. Small disturbances of atomic vibrations are still challenging to be sensitively detected. It requires a reliable coupling between individual phonons and internal electronic levels that light can readout. As available information in a few electronic levels about the phonons is limited, the coupling needs to be sequentially repeated to further harvest the remaining information. We analyze such phonon measurements on the simplest example of the force and heating sensing using motional Fock states. We prove that two sequential measurements are sufficient to reach sensitivity to force and heating for realistic Fock states and saturate the quantum Fisher information for a small amount of force or heating. It is achieved by the conventionally available Jaynes–Cummings coupling. The achieved sensitivities are found to be better than those obtained from classical states. Further enhancements are expectable when the higher Fock state generation is improved. The result opens additional applications of sequential phonon measurements of atomic motion. This measurement scheme can also be directly applied to other bosonic systems including cavity QED and circuit QED.
Quantification of existing rooftop PV hourly generation capacity and validation against measurement data

Abstract The spatio-temporal patterns of the existing rooftop photovoltaic (PV) production can provide valuable insights for the design of effective strategies for PV integration at large scale. In this work, we quantify the hourly production of existing rooftop PV installations by combining two large-scale methods, for detecting rooftop solar panels and for estimating their hourly PV generation, which are both applicable at the Swiss national scale. A validation against measured data of PV installations from 16 roofs in the Swiss Canton of Aargau shows that the existing PV area is detected accurately, while the hourly profiles overestimate the PV production, with higher errors in winter than in summer. These errors result in a mean overestimation of the annual PV production by 16%, assuming a triangular panel placement at low tilt (15°) on flat roofs.
Sparse Fusion for Multimodal Transformers

Multimodal classification is a core task in human-centric machine learning.We observe that information is highly complementary across modalities, thus unimodal information can be drastically sparsified prior to multimodal fusion without loss of accuracy.To this end, we present Sparse Fusion Transformers (SFT), a novel multimodal fusion method for transformers that performs comparably to existing state-of-the-art methods while having greatly reduced memory footprint and computation cost. Key to our idea is a sparse-pooling block that reduces unimodal token sets prior to cross-modality modeling.Evaluations are conducted on multiple multimodal benchmark datasets for a wide range of classification tasks. State-of-the-art performance is obtained on multiple benchmarks under similar experiment conditions, while reporting up to six-fold reduction in computational cost and memory requirements. Extensive ablation studies showcase our benefits of combining sparsification and multimodal learning over naive approaches. This paves the way for enabling multimodal learning on low-resource devices.
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling

In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations. Notably, the resulting global model excels in personalization as \texttt{FGPR} jointly learns a global $\mathcal{GP}$ prior across all clients. The predictive posterior then is obtained by exploiting this prior and conditioning on local data which encodes personalized features from a specific client. Theoretically, we show that \texttt{FGPR} converges to a critical point of the full log-likelihood function, subject to statistical error. Through extensive case studies we show that \texttt{FGPR} excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.
Iterative methods for solving managed problems of the television image processing

The article describes the most important issues of the formation of digital television images. They are based on improving the control system for image processing processes, methods of numerical models and algorithms for solving problems of filtering various digital television images using Fourier and Wavelet methods. In this area, targeted scientific research has been carried out. It is aimed at improving the method of classification and selection of criteria for monitoring and evaluating image quality, methods for controlling image clarity at given values of medium-intensity pixels, creating algorithms for modeling the image processing process. This allows perfecting the methods for controlling the processes of ensuring the level of clarity of the TV image.
PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing

Vision Transformer (ViT) is known to be highly nonlinear like other classical neural networks and could be easily fooled by both natural and adversarial patch perturbations. This limitation could pose a threat to the deployment of ViT in the real industrial environment, especially in safety-critical scenarios. In this work, we propose PatchCensor, aiming to certify the patch robustness of ViT by applying exhaustive testing. We try to provide a provable guarantee by considering the worst patch attack scenarios. Unlike empirical defenses against adversarial patches that may be adaptively breached, certified robust approaches can provide a certified accuracy against arbitrary attacks under certain conditions. However, existing robustness certifications are mostly based on robust training, which often requires substantial training efforts and the sacrifice of model performance on normal samples. To bridge the gap, PatchCensor seeks to improve the robustness of the whole system by detecting abnormal inputs instead of training a robust model and asking it to give reliable results for every input, which may inevitably compromise accuracy. Specifically, each input is tested by voting over multiple inferences with different mutated attention masks, where at least one inference is guaranteed to exclude the abnormal patch. This can be seen as complete-coverage testing, which could provide a statistical guarantee on inference at the test time. Our comprehensive evaluation demonstrates that PatchCensor is able to achieve high certified accuracy (e.g. 67.1% on ImageNet for 2%-pixel adversarial patches), significantly outperforming state-of-the-art techniques while achieving similar clean accuracy (81.8% on ImageNet). Meanwhile, our technique also supports flexible configurations to handle different adversarial patch sizes (up to 25%) by simply changing the masking strategy.
Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver

Artificial intelligence (AI)-based image analysis is increasingly being used for preclinical safety-assessment studies in the pharmaceutical industry. In this paper, we present an AI-based solution for preclinical toxicology studies. We trained a set of algorithms to learn and quantify multiple typical histopathological findings in whole slide images (WSIs) of the livers of young Sprague Dawley rats by using a U-Net-based deep learning network. The trained algorithms were validated using 255 liver WSIs to detect, classify, and quantify seven types of histopathological findings (including vacuolation, bile duct hyperplasia, and single-cell necrosis) in the liver. The algorithms showed consistently good performance in detecting abnormal areas. Approximately 75% of all specimens could be classified as true positive or true negative. In general, findings with clear boundaries with the surrounding normal structures, such as vacuolation and single-cell necrosis, were accurately detected with high statistical scores. The results of quantitative analyses and classification of the diagnosis based on the threshold values between "no findings" and "abnormal findings" correlated well with diagnoses made by professional pathologists. However, the scores for findings ambiguous boundaries, such as hepatocellular hypertrophy, were poor. These results suggest that deep learning-based algorithms can detect, classify, and quantify multiple findings simultaneously on rat liver WSIs. Thus, it can be a useful supportive tool for a histopathological evaluation, especially for primary screening in rat toxicity studies.
A discussion of S.M. Krylov’s book «Neocybernetics» (2008)

A comparative analysis of the “general formal technology (GFT)” by S. M. Krylov is carried out in the context of the published book of the authors “General Theory of Technologies and Microelectronics” (2020) and on the basis of his work of 2008. Despite the abstractness of the algebraic-algorithmic approach, Krylov offers a number of specific constructions that are in demand during the fourth industrial revolution and for the future development of industrial technology in nanoelectronics and biotechnology. Industrial technology is considered as a complex object of management, i.e., it is the object of study of the new discipline «neocybernetics». Although the foundations of this approach were laid in 1930s–1960s within the framework of logical and mathematical research, its expansion is inevitable when using self-organization processes to obtain functional supramolecular structures in technological processes of nanoelectronics (for example, DNA origami engineering). The issues of complexity quantification for a product itself (structure) and its manufacturing technology, or, according to Krylov, the complexity of technological automata, have become even more relevant than before. The theoretical issues of self-organization, the development of artificial life, and the creation of self-replicating technical systems also seem promising for solution. In our opinion, Krylov’s formal technology is an important “block” in the advancement of general theory of technologies (GTT) useful for describing the technology at the levels: operation, route, and process. We would like to encourage a wide range of readers to study the book and form a steady interest in general technological issues. The value of GTT and GFT extends beyond the sphere of technology and, in a narrow sense, factory production, but also into the area of «fine» regulation of physiology in biological objects and pharmacy, as well as into the problem field of cognitive sciences, psychology, and education. when the focus is on the personality structure and heterogeneous constructs «floating in the sea of the unconscious». Both S.M. Krylov and we demonstrate that the issues of industrial technology cannot be considered without abstract formalization and without reference to philosophy.
ANALYSIS OF THE LEVEL AND FORM OF FORMATIVE ASSESSMENT IN ONLINE CLASSES OF THE PRIMARY AND LOWER SECONDARY SCHOOL IN THE CZECH REPUBLIC

The current form of formative assessment in Czech primary and lower secondary schools is facing criticism. This is evidenced by the reports of the Czech School Inspectorate and some research findings of the authors. Despite the proven benefits of formative assessment not only abroad, teachers in Czech primary and lower secondary schools, according to current research, still prefer summative assessment. There is not much evidence of the current form of formative assessment in Czech schools because only a small amount of research deals with the issue of formative assessment. Furthermore, formative assessment has not yet been properly grasped in the national curricula. However, the general assumption is that teachers do work with formative assessment, but it has not yet been revealed in what form and extent.The article and the research present the results of the analysis of ten online lessons at primary and lower secondary school within the educational area of Language and Communication through Language. The analysis was performed using a tool based on the concept of formative assessment by McMillan. This concept was adjusted and edited by the authors of this paper to be based on the current educational reality of Czech primary and lower secondary schools. The tool works with three levels of formative assessment which can reveal the final form of formative assessment in teaching. The main goal is to present a research tool and the results of 10 analyzed online lessons. Online lessons were recorded and their results were thoroughly analyzed by more observers. We monitored the degree of consensus between the two observers.It was found that the teaching of primary and lower secondary school teachers reflects a low level of formative assessment with a slight overlap to a moderate level. The results of this study thus confirm the current criticism of schools that primary and lower secondary school teachers rather prefer summative assessment. The results will serve the primary and lower secondary school in the effective implementation of formative assessment in teaching (the goal is to achieve a higher level of formative assessment). The results can also be of value to the researchers who deal with the issue of implementing formative assessment into teaching.
An integrated information environment to support geophysical research of the Baikal Rift Zone

In this paper it is proposed to organize information support for geophysical research of the Baikal Rift Zone by organizing an integrated system available on the Internet. The system provides access to experimental data and their computational analysis, and also provides systematization of information objects of the subject area based on the ontology of the subject area.
Human Gait Analysis Method Based on Sample Entropy Fusion AlphaPose Algorithm

Aiming at the problem of difficulty to evaluate the recovery of patients after their joint replacement accurately, a new gait analysis method was proposed on the basis of Sample Entropy fusion AlphaPose. In the algorithm, AlphaPose was encouraged to extract the trajectory of the key points from healthy people and patients, and convert the transform trajectories into feature vectors. After normalized, feature vectors were calculated and analyzed by Sample Entropy algorithm. The results demonstrated that there was significant difference in the entropy value of heel key point waveform between two groups, the entropy value of patients with joint disease was lower than that of healthy group. Comparing with the traditional gait analysis method, the proposed algorithm performs better on human pose recognition, because of its strong robustness and efficiency.
ANALYZING IF A STRIKE CAN AFFECT OVER THE ACADEMIC PERFORMANCE OF STUDENTS CONSIDERING A SUCCESSION OF MATHEMATIC COURSES

J. Figueroa-González, S.B. González-Brambila, L. Sánchez-Guerrero, B.A. González-BeltránUniversidad Autonoma Metropolitana Azcapotzalco (MEXICO)
A comparative analysis of SOAP and REST Web service composition based on performance in local and remote Cloud environments

Web services are an attracting area that interest many researchers and industrial organizations. Given the convenience and reusability, web services have become a main mode of cloud application. Web services can be developed based on two interaction styles such as Simple Object Access Protocol (SOAP) and Representational State Transfer Protocol (REST). Nevertheless, the function of single web service is usually too simple to satisfy the user’s complex demand. Therefore, it is necessary to combine a set of basic web services in order to create a composite web services. However, most existing work on the comparison of SOAP and REST focuses on a single micro-service and fails to outline the performance of those two approaches in a real-case situation where, most of the time, multiple micro-services are bound together to provide a more holistic answer to user requests. In this paper, we try to tackle this issue from a more business-centered perspective. We use a case study involving multiple services both in local and remote Cloud environments with SOAP Web services composition and REST Web services composition. The first environment will help us find out the behavior in a more controlled situation compared to the latter where multiple service providers are involved.
Sedimentological and Heavy Metals Characteristics of Streets Dust in some Areas East of Baghdad for 2020

Road-side dust samples were collected during August in 2020 from selected areas of, Al-Rusafa, Baghdad, Iraq. A sedimentological and mineralogical analysis of street dust was conducted. Three areas were selected to study street dusts which are Al-Baladitat, Al-Obaidi and Ziona. The laboratory analyses were done in the Department of Geology, College of Science, University of Baghdad. The heavy metal contents were determined in the roadside dust using XRF Method. It was found that the dust is of muddy texture, and is believed to be transmitted with the various storms blowing on Baghdad or by the wheels of Cars. The results of mineralogical investigation revealed that the dust samples composed of quartz, feldspar, calcite, gypsum and sedimentary rocks fragments, as light minerals and the heavy minerals were found as garnet, epidote, chlorites, pyroxene, biotite, zircon, muscovite, hornblende, tourmaline. The heavy metals (Fe, Co, Ni, Cu, Zn and Pb) in these streets dust samples were studied and used as indicator for pollution. two o main indices are applied: contamination factor, and pollution load index. The contamination factor for Co, Zn, Pb, and Ni is classified as class 2, which indicate moderately contamination, while the contamination factor for Fe and Cu is classified as class 1, which indicate low contamination. The pollution load index values in the all of studied sites are classified as class 2 (Deterioration on site quality) indicating local pollution, as well as denote perfection with class 0 of no pollution.
MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video

Self-attention has become an integral component of the recent network architectures, e.g., Transformer, that dominate major image and video benchmarks. This is because self-attention can flexibly model long-range information. For the same reason, researchers make attempts recently to revive Multiple Layer Perceptron (MLP) and propose a few MLP-Like architectures, showing great potential. However, the current MLP-Like architectures are not good at capturing local details and lack progressive understanding of core details in the images and/or videos. To overcome this issue, we propose a novel MorphMLP architecture that focuses on capturing local details at the low-level layers, while gradually changing to focus on long-term modeling at the high-level layers. Specifically, we design a Fully-Connected-Like layer, dubbed as MorphFC, of two morphable filters that gradually grow its receptive field along the height and width dimension. More interestingly, we propose to flexibly adapt our MorphFC layer in the video domain. To our best knowledge, we are the first to create a MLP-Like backbone for learning video representation. Finally, we conduct extensive experiments on image classification, semantic segmentation and video classification. Our MorphMLP, such a self-attention free backbone, can be as powerful as and even outperform self-attention based models.
Thermal entanglement in a double Jaynes–Cummings model

In this paper, we have investigated the entanglement dynamics between two initially entangled qubits interacting with two independent thermal cavities modes in the framework of resonant double Jaynes&ndash;Cummings model. We obtain the exact solution for the model under consideration and derive the time dependence of the atom-atom negativity. Results reveal that for entangled atoms the initial atomic quantum correlations can partially restore its original values for a finite interval of time, even for relatively high cavity temperatures.
VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion

Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view information by global averaging, thus losing fine detail, or they can heuristically cluster views for local fusion, thus restricting their ability to consider all views jointly. Our key insight is that greater detail can be retained without restricting view diversity by learning a view-fusion function conditioned on camera pose and image content. We propose to learn this multi-view fusion using a transformer. To this end, we introduce VoRTX, an end-to-end volumetric 3D reconstruction network using transformers for wide-baseline, multi-view feature fusion. Our model is occlusion-aware, leveraging the transformer architecture to predict an initial, projective scene geometry estimate. This estimate is used to avoid backprojecting image features through surfaces into occluded regions. We train our model on ScanNet and show that it produces better reconstructions than state-of-the-art methods. We also demonstrate generalization without any fine-tuning, outperforming the same state-of-the-art methods on two other datasets, TUM-RGBD and ICL-NUIM.