Reviewerly/exHarmony huggingface dataset:
Empirical Study One-stage Object Detection methods for RoboCup Small Size League

Small Size League (SSL) is a division of the traditional RoboCup, founded to promote research in robots and AI. A fast and accurate real-time object detection model is essential for RoboCup SSL soccer robots, serving the design and development of competitive strategies. Specific state-of-the-art object detection methods have reported inference speed up to 94 FPS on the SSL open-source benchmark dataset, but only at intermediate accuracy. Considering the advancement in deep learning methods for feature extraction and object detection, in this paper, we conducted surveys and experiments on one-stage object detection methods provided in the MMDetection framework on the dataset for RoboCup SSL. YOLOX-tiny model achieved 58.60% AP, which is significantly higher than baseline methods, while maintaining an acceptable inference speed of 37 Frames Per Second (FPS). Other state-of-the-art one-stage methods have achieved very high performance, up to 74,10% Average Precision (AP). However, certain methods did not meet the minimum inference speed requirement of real-time object detection.
Fast Phrased Search for Encrypted Cloud Storage

In the cloud computing world, access and storage of confidential and sensitive documents have been a challenge. Data Breach is one of the major concerns in the cloud-computing world. Secure access to the data is crucial in order to protect personal information. Usually, untrusted cloud providers after the encryption of files maintain the private keys with themselves instead of data owners, which leads to data leakage and no privacy to the users. Hence, there is a requirement to securely store data on public, private, or Hybrid clouds without any use of private keys. In this paper, the aim is to provide a technique which helps in the conjunctive key search of encrypted documents, with the help of Bloom filters, which is faster, scalable, secure, and does not require any private key storage. The cloud facility will only contain a list of bloom filters with the Bloom Filter Identification numbers (IDs), which aids in privacy of the user.
Perception Testing in Fog for Autonomous Flight

As the path towards Urban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). This paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.
Russian higher education at a crossroads: one step forward, two steps back

The article presents the results of research conducted by the staff of the Research Institute “New Economy and Business” of Plekhanov Russian University of Economics concerning the role of the Russian educational system in the space of the life of society, where it occupies an extensive sphere of intertwining direct and indirect interests, processes, actions of various parties: from an individual to the entire population of the country; from individual enterprises, organizations, to the entire production; from the employee to the sphere of labor, the entire socio-economic complex of the country. The system of higher education in the format of the Bologna system has been so deeply transplanted by many years of efforts into the entire vast sphere of Russian education, integrated with the spheres of science and labor. society as a whole, that it is difficult even to imagine not only an integral, but even a fragmentary one-time abolition of it. Based on almost 50 years of experience in serving Russian education in the teaching, scientific, and administrative fields, the authors of the article express their opinion regarding the demands seriously expressed in the Russian scientific and pedagogical community for the immediate abolition of the current system of Russian higher education (meaning the Bologna type) and the transition to a new, bearing the features of national tradition. The article notes that currently, the mechanism of simultaneous cancellation and transfer of the system is not yet completely clear. In the final section of the article, conclusions are drawn concerning some aspects of the fate of the Bologna system in the Russian Federation, other innovations of recent years, including the Western model in higher education, based on the experience of previous higher school reforms.
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formulate a multi-resolution framework which identifies U-Nets as finite-dimensional truncations of models on an infinite-dimensional function space. We provide theoretical results which prove that average pooling corresponds to projection within the space of square-integrable functions and show that U-Nets with average pooling implicitly learn a Haar wavelet basis representation of the data. We then leverage our framework to identify state-of-the-art hierarchical VAEs (HVAEs), which have a U-Net architecture, as a type of two-step forward Euler discretisation of multi-resolution diffusion processes which flow from a point mass, introducing sampling instabilities. We also demonstrate that HVAEs learn a representation of time which allows for improved parameter efficiency through weight-sharing. We use this observation to achieve state-of-the-art HVAE performance with half the number of parameters of existing models, exploiting the properties of our continuous-time formulation.
Wavelet Score-Based Generative Modeling

Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient depends on some probabilistic score. The discretization of such SDEs typically requires a large number of time steps and hence a high computational cost. This is because of ill-conditioning properties of the score that we analyze mathematically. We show that SGMs can be considerably accelerated, by factorizing the data distribution into a product of conditional probabilities of wavelet coefficients across scales. The resulting Wavelet Score-based Generative Model (WSGM) synthesizes wavelet coefficients with the same number of time steps at all scales, and its time complexity therefore grows linearly with the image size. This is proved mathematically over Gaussian distributions, and shown numerically over physical processes at phase transition and natural image datasets.
MEDIA X-RECORDER DAN YOUTUBE UNTUK MENINGKATKAN MOTIVASI BELAJAR BAHASA INDONESIA MODA DARING

This study describes the use of X-Recorder and Youtube media applications and explains how the effectiveness of using X-Recorder and Youtube media in increasing student motivation in learning Indonesian at MTsN 4 Jombang. The research method used is descriptive qualitative. This research was conducted on class VII students of MTsN 4 Jombang. The research was conducted for two cycles with several stages which included planning, implementing actions, observing activities, and reflecting. Based on the results of the research, the enthusiasm of students in carrying out online learning has increased significantly so that it can be said that the application of X-Recorder and Youtube media can increase student motivation because students can feel the presence and guidance of the teacher so that learning is more directed and meaningful. This research concludes that the application of X-Recorder and Youtube media can increase students' learning motivation at MTs.Negeri 4 Jombang. Student learning outcomes are strongly influenced by the quality of learning implemented in madrasas. One that determines the quality of learning is the use of appropriate learning media and also the presence of a teacher. One of the learning media that can be applied to online learning is the X-Recorder and Youtube media
Two methods for determining combinatorial identities

Two methods are presented for determining advanced combinatorial identities. The first is based on extending the original identity so that it can be expressed in terms of hypergeometric functions whereupon tabulated values of the functions can be used to reduce the identity to a simpler form. The second is a computer method based on Koepf's version of the Wilf-Zeilberger approach that has been implemented in a suite of intrinsic routines in Maple. As a consequence, some new identities are presented.
Preface: 7th International Conference on Basic Sciences 2021 (ICBS 2021)

First Page
EPILOGUE

This chapter highlights the relationship between imagination and science. It also covers the notion of science and imagination during the period of Romanticism and Enlightenment. Under the context of scenario planning, imagination was a critical element in its emergence and had been perceived as a solution to problems that could not be solved with the available scientific tools. Then, scenario methods were reconstructed in terms of psychocognitive, behavioral, and quantitative scientific discourses and embedded in modern scientific rationale to improve the practice's scientific legitimacy. The chapter explains the history of scenario planning expressed three axes in the problematization of the relationship between science and imagination. Moreover, imagination is accepted as a basis for scientific practices for researching the unknown future.
Change Bit-Depth Procedure for Data Measured Preprocessing before Compression

The paper proposes to change the bit-depth of the original data sequence as a preprocessing of the measuring data, which is carried out before the compression procedure. In this case the digit capacity conversion is performed without accounting for the primary data sample boundaries. The simulation was carried out in the Matlab software environment. The findings of the authors’ research are presented. They prove to be a promising approach for some types of the studied data at least and several digit capacity values. The analysis of autocorrelation function values showing the data correlation revealed by the conversion is used as an assessment of perspective of the active conversion. In this article the authors review the values of secondary maximums of autocorrelation function. Their occurrence shows the demonstration of the implied correlative correspondence. Such correlations can potentially allow the converted data to be compressed more effectively than the original data using the known compression methods.
PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

This paper describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization. This paper provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.
Leptogenesis and muon $\boldsymbol{(g-2)}$ in a scotogenic model

We present a detailed study of a scotogenic model accommodating dark matter, neutrino masses and the anomalous magnetic moment of the muon while being consistent with the existing constraints on flavour violating decays of the leptons. Moreover, this model offers the possibility to explain the baryon asymmetry of the Universe via leptogenesis. We determine the viable regions of the model's parameter space in view of dark matter and flavour constraints using a Markov Chain Monte Carlo setup combined with a particular procedure to accommodate neutrino masses and the anomalous magnetic moment of the muon at the same time. We also discuss briefly the resulting collider phenomenology.
The performance sediment silica adsorbent with the effect of concentration and contact time for analysis Pb2+ ion

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Anugrah Ricky Wijaya, Devi Barkhatin Nurjanah, Irma Kartika Kusumaningrum, Surjani Wonorahardjo,, Rina Tri Vidia Ningsih; The performance sediment silica adsorbent with the effect of concentration and contact time for analysis Pb2+ ion. AIP Conference Proceedings 24 January 2023; 2634 (1): 020037. https://doi.org/10.1063/5.0112324 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code, which often requires specific treatment for each platform.The problem becomes critical on embedded devices, where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work, a novel backend for the Open Neural Network Compiler (ONNC) is proposed, which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange (ONNX) models.Several novel optimization techniques are also incorporated in the backend, such as quantizing the ONNX model's weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications: handwritten digit recognition on the Modified National Institute of Standards and Technology (MNIST) dataset and model, and image classification on the Canadian Institute For Advanced Research and 10 (CIFAR-10) dataset with the AlexNet-Light model.The system achieves 98.90% and 90.55% accuracy for handwritten digit recognition and image classification, respectively.Furthermore, the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective, this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.
A Cross-Plane Color Image Encryption Algorithm Based on 1D-SLM

With the rapid development of 5G technology, it has become fast and easy for people to transmit information on the Internet.Digital images can express information more intuitively, so transmitting information through images has excellent applications.This paper uses a new chaotic system called 1D-Sin-Logistic-Map (1D-SLM).1D-SLM has two control parameters, which can provide larger parameter space, and the parameter space in the chaotic state is continuous.Through Lyapunov exponent analysis (LE), bifurcation diagrams analysis, spectral entropy analysis (SE), and 0-1 test, it is verified that 1D-SLM has complex dynamic behavior and is very suitable for cryptography.Compared with other 1D chaotic systems, the 1D-SLM has a larger Lyapunov exponent (LE) and spectral entropy (SE).For color image encryption algorithms, only relying on chaotic mapping is not enough to ensure security.So combined with 1D-SLM, we design a color image encryption algorithm, which is implemented by plane expansion, which reduces the correlation between the three channels of color images.The experimental results show that the proposed cross-plane color image encryption algorithm is safe and resistant to common attack methods.
Sistem Pelaporan Pengolahan Sampah Berbasis Web Pada Dinas Lingkungan Hidup Kota Metro

The barrier to cleanliness in the environment is garbage that causes environmental pollution and will eventually damage the environment. So far, waste processing is carried out in a conventional manner, namely collection, transportation and final disposal at the final disposal site (TPA). Waste data processing includes data on achievement of waste reduction, data on achievements in reducing waste and producing a waste processing balance. Waste processing is very important for Metro City Environmental Service because it becomes an annual report submitted to the Mayor of Metro. Currently all waste processing data is conducted by using the Microsoft Excel program. Because the waste processing data are interrelated, it is very possible for processing with the application because it will be able to integrate the data, and formulas can be made in coding to avoid changing formulas when done in Excel, and it is made only once. The applications built is web-based with a structured programming approach, software development methods using SDLC Method. Software testing is using black box testing. Based on these problems, an application that can help process waste data processing at the Metro City Environmental Service is needed.
A Proposal for Deepening the self defense of Korean military through the surrender of Japanese in the Imjin war a study on the martial arts

The purpose of this study is to present an in-depth plan for modern military self-defense technology education based on the military use of Japanese surrender during the Imjin War. The results of the study are as follows. First, Joseon recorded many defeats due to the trend of neglecting martial arts training and the poor military organizational system. Second, King Seonjo is in charge of training the Adongdae, a children
NodeSig: Binary Node Embeddings via Random Walk Diffusion

Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdis-ciplinary applications. As the scale of networks increases, most of the widely used learning-based graph representation models also face computational challenges. While there is a recent effort toward designing algorithms that solely deal with scalability issues, most of them behave poorly in terms of accuracy on downstream tasks. In this paper, we aim to study models that balance the trade-off between efficiency and accuracy. In particular, we propose Nodesig, a scalable model that computes binary node representations. Nodesig exploits random walk diffusion probabilities via stable random projections towards efficiently computing embeddings in the Hamming space. Our extensive experimental evaluation on various networks has demonstrated that the proposed model achieves a good balance between accuracy and efficiency compared to well-known baseline models on the node classification and link prediction tasks.
LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets.
An analysis method for predicting breast cancer using data science processes and machine learning

In two decades, the number of people with breast cancer has almost doubled: in 2000, about 10 million patients had the disease; by 2020, it had reached 19 million. It is estimated that one in five people today will develop some form of cancer in their lifetime. Studies suggest that the number of people diagnosed with cancer will increase in the coming years, being approximately 50% higher in 2040 than in 2020. This article provides an analysis method to predict or diagnose breast cancer using data science processes and machine learning. The analysis method is structured into three phases. The first one is a data preparation phase, the second one is a predictive analysis phase, and the last one is an evaluation metric. Therefore, the predictions are experimented with machine learning techniques, which are: KNN, gradient boosting classifier, and random forest, for which evaluation metrics are presented with the next quality measures: Accuracy, Precision, Recall, and F1-Score. The dataset selected for this phase of analysis is Wisconsin breast cancer [1]. These data analysis techniques can be extended to other learning techniques and can also be used in future scientific work such as disease prediction or medicine in general.