Reviewerly/exHarmony huggingface dataset:
Dangerous items recognition and face recognition system based on K210 and YOLO

With the rapid development of artificial intelligence technology, dangerous items detection and face detection industry has gradually become the mainstream application technology, if you are using a general-purpose processor face detection for image data processing which is complete, it will produce a certain time, and there will be a single function, low computing power, low efficiency and so on. In this paper, a KPU neural network accelerator based on K210 chip is designed. The neural network model calculation of dangerous items detection and face detection is completed by YOLO network, and the functions of dangerous items detection and face detection are quickly realized. Experimental results show that the detection speed of the system is fast and the recognition result is more accurate.
Research on Application of Computer Image Recognition Technology in Kitchen Hygiene Inspection

According to the detection results of kitchen pollutants by environmental testing stations, computer image recognition technology is used to evaluate their exposure. Through the three aspects of risk identification, exposure evaluation, and risk characterization, the measurement results are compared and analysed with the standard values, and the exposure intensity of the pollutants is calculated. We get the daily potential exposure doses for people of different age groups. At the same time, based on the user's daily family life safety needs, a reliable and practical remote kitchen hygiene inspection safety system is designed using computer image recognition technology.
Synergy Analysis of One-and-a-Half Century Shallow Mature Oil Field in Eastern Carpathians

Summary This study demonstrates the effectiveness of using complementary geophysical and geological methods to better understand the structure and petroleum potential in a mature, late life producing oil field in the internal part of the Romanian Eastern Carpathains, in the so-named Tisza-Dacia block. 161 years ago exploration work started and, following positive results, several wells were drilled. Crude oil extraction started as early as 1899 and continues until present, with several interruptions. Without serious advanced exploration activities and mainly based on interpretation of poor well cores, the extraction was a temptation despite of the not-so-significant oil quantities. Several wells reported basement at shallow depth between D.V. and S.S. crustal faults and so the hydrocarbon prospective was considered as limited. 15 y ago some the new 2D seismic was of modest quality, so resistivity, grav and mag plus MT data were added over geochemical sampling and geological mapping. This is an example on how a historic production could be boosted by integration of geophysical with non-geophysical methods so that the resulted geological model is modernly calibrated. The seismic data reprocessing by better velocity picking, statics and migration was based on a mixture of geodata.
(Online) Analysis of High Resolution Aeromagnetic Data of Birnin Gwari and Environs for Delineation of Gold Mineralization Potential Zones

Summary High resolution aeromagnetic data was analysed and interpreted both qualitatively and quantitatively to delineate the occurrence of porphyry mineral deposits (Gold) in Birnin Gwari and environs, North-Western, Nigeria. The Residual Magnetic Intensity (RMI) anomalies extracted were reduced to equator, thereafter enhancement procedures (First Vertical Derivative (FVD), Tilt-angle Derivative (TDR) and Analytical Signal (AS)) were carried out on the resulted reduced to equator grid of the RMI to improve the quality of the aeromagnetic data for better understanding of the subsurface geology. The RMI revealed several geologic structures which are related to dykes/faults striking predominantly in the NS, NNE-SSW, NE-SW and NW-SE directions. Geological structures delineated as high amplitudes in the analytical signal derived map which are the most significant concentration of mineralization zones in the study area established a positive correlation with CET grid analysis while the CET porphyry analysis map highlights high occurrence of circular and near circular features (porphyry like features). The depth estimation applied using analytic signal and Source Parameter imaging (SPI) techniques show comparable results. The depth to the top of the basement sources ranges from 933 to about 2664 m in the study area.
Case Study of Geothermal Resource Exploration in Hongye Area, Hualien

Summary In response to global climate change, the government promotes energy transformation and 2050 net-zero emission policies. Under the forward-looking infrastructure plan, it plans to &amp;quot;enhance the comprehensive geothermal resource exploration and information supply plan&amp;quot;, with the purpose of achieving energy transformation, creating green energy that the country will need in the future and promoting green energy infrastructure for environmental sustainability. The 2050 net-zero emissions plan is that renewable energy will account for 60–70% of total electricity in 2050. The short-term goal is to achieve low carbon, by reducing energy use and non-energy use carbon emissions, and carrying out energy transformation, increasing green energy, including geothermal energy. The long-term goal is to develop towards zero carbon, including the adjustment of energy, industrial structure and social lifestyle. Therefore, geothermal power generation, which can be used as base-load power, will play an important role in the future. The geothermal power plant development process includes: preliminary survey, resource exploration, drilling survey, feasibility assessment, site development, plant construction, and plant operation and maintenance (IGA Service GmbH, 2014). At this stage, the Central Geological Survey (CGS) is conducting basic exploration of geothermal potential areas, using emerging technologies to expand the scope and depth of exploration, so as to grasp the basic geological information of geothermal resources, and collect geothermal exploration data from various units to establish a geothermal exploration information platform. The CGS provides those who are interested in geothermal development to have access to underground geological information, so as to reduce the exploration risk in the early stage of geothermal development and shorten the development time. Starting from 2021, a key survey area “Ruilin-Hongye area” was selected for further exploration and investigation in many areas with geothermal potential in the Hualien area. In this project, the collection of past literature data and new survey data are integrated, and a conceptual model of geothermal geology in the Ruilin-Hongye area is initially proposed. Continuing with the measurement results of magnetotellurics in the key survey area, the volumetric evaluation method is used to evaluate the amount of geothermal resources.
On the Effective Representation of Quantum Data for Classical Machine Learning Problems

There is no doubt that quantum computing has opened up new horizons and perspectives in many fields, including scientific research. With the advancement of technology and quantum computers, we can now conduct new kinds of scientific experiments: observing quantum properties of individual systems, atoms, electrons and photons as well as influencing and controlling quantum systems. Quantum mechanics describes the properties of quantum systems by their quantum states. Unlike in classical mechanics, quantum states themselves cannot be directly observed in experiments, so many-body dynamics mathematical simulations are useful. Even though many-body dynamics problems can be solved for limited cases, but when we consider real-world pieces of matter this leads to computation-intensive calculations that are beyond classic digital computers. The memory and time needed to describe the quantum state of a many-body system scales exponentially with the size of the system. This has led physics communities to focus their attention on the algorithms underlying modern machine learning with the goal of making progress in quantum matter research. As a consequence, the successful application of machine learning requires effective and informative data representation. This paper discusses some novel aspects of applying classical machine learning algorithms to quantum data derived from measurements associated with quantum systems in many-body simulation problems. In this paper, we investigate and experimentally demonstrate one of the effective methods of the quantum data representation suited for the classical machine learning applications based on quantum classical shadows.
Implementing Mail Administration Using the Codeigniter Framework

Administration is the activity of systematically managing and recording data information to provide various information and also make it easier to retrieve it either partially or completely. The administration of mailing at SMK Negeri 1 Glagah is still conventional by recording incoming and outgoing mail using a book and storing the mail in an uncertain places. Some of the problems that can be solved with information technology, one of which is the storage of incoming and outgoing mail at SMK Negeri 1 Glagah. With a mail administration application that uses the CodeIgniter Framework and MySQL database, teachers who being admins or operators can directly enter incoming and outgoing mail data in the application, and they can directly distribute the mail to the receiver person through the application. Teacher who receive the mail will get a notification on the application if there is a new mail coming in. With the Mail Administration Application, staff and school principals can find out the existing mail data easily and computerized with accurate and archived data. The results of the questionnaire recapitulation can be concluded that the overall application quality level is in Good criteria, with a percentage of 84.33%.
Authentication of Electronic Version of the Holy Quran: A Review

The development of information technology has resulted in an increase in the number of electronic versions of the Holy Quran being made available. The Holy Quran is now available in an electronic version that can be read online on devices such as personal computers, tablets, and mobile phones. This version was produced by a number of different developers. The introduction of an electronic version of the Holy Quran presents an imminent danger to the religion of Islam, despite the fact that such proliferation makes it possible to spread Islam to a wider audience. It has been discovered that there is a forged or counterfeit electronic version of the Quran. The majority of the dangers could be eliminated if there was a way to authenticate electronic copies of the Holy Quran in a secure manner. It is of the utmost importance to find a solution that will ensure that the electronic version of the Holy Quran maintains both its content and its integrity. This study presents a discussion of the current state of research, a research survey, and an overview of state-of-the-art research related to the authentication of the Holy Quran. In its conclusion, this study provides a summary of the challenges, methods, and opportunities involved in authenticating electronic versions of the Quran.
Promoting Accuracy in Automatic Grading of Low-Amplification Histopathology: With Augmentation and a Multi-Dilation Model

Advances in artificial intelligence have facilitated the automated grading of histopathology slides. Yet, the amplification of whole slide scanners (WSS) restrains the resolution and thus the accuracy in patch-based grading. In this work, we find that augmentation can significantly promote performances under this issue. With augmentation and a multi-dilation model, the CovXNet [1], we yielded a balanced accuracy of 91.84% and an F1 score of 91.84%, which is the current highest for the Breast Histopathology Dataset (BHI, amplification 40X). However, our work elucidated that whether augmentation assisted with this task, where information concentration is low, highly relies on the model’s structure and generalization capacity, especially the deeper models. ResNet, DenseNet and tested attention models are not synergized with augmentation, in spite of previous excellence with this dataset [2]. A simple CNN network [3] is surprisingly improved markedly. The reason lies on the models’ structure.Additionally, this work also found two types of common errors in high-starred codes, when using integrated shuffle functions, which fake a higher accuracy by masking class 0 as class 1. Both shuffle functions in train_test_split and ImageDataGenerater were tested labile. Using Sklearn.shuffle respectively is safer.
Federated Visualization: A Privacy-Preserving Strategy for Aggregated Visual Query

We present a novel privacy preservation strategy for aggregated visual query of decentralized data. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federation of visualization is fulfilled by leveraging a shared global module that composes the encrypted externalizations of transformed visual features of data pieces in local modules. We design two implementations of federated visualization: a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prediction-based</i> scheme, and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">query-based</i> scheme. We demonstrate the effectiveness of our approach with a set of visual forms, and verify its robustness with evaluations. We report the value of federated visualization in real scenarios with an expert review.
About Maria Osipovna Knebel and her method of effective analysis

May 2023 marks the 125th anniversary of Maria Osipovna Knebel’s birth. On the eve of this anniversary, Natalya Alekseevna Zvereva, Knebel’s student, outstanding theatre teacher, professor at the Department of Drama Directing at GITIS, Honored Art Worker of the Russian Federation, and Ph.D., shared her memories of working together with Maria Osipovna in 1971. It happened during the production of the play by A. N. Ostrovsky, “Talents and Admirers,” at the Moscow Academic Theatre named after Vl. Mayakovsky. Maria Knebel called this performance the most beloved of all the productions staged by her. Using the example of Knebel’s work on this production, the article traces some unique features of her rehearsal and pedagogical approach. Her method of work was based on an effective analysis of the play. She obtained this last brilliant discovery of K.S. Stanislavsky (as Knebel herself described it) directly from him. Maria Knebel devoted many years of her life to this method’s development, along with its introduction into theatrical practice and pedagogy.
Grammatical Evolution with Code2vec

This paper concerns using the code2vec vector embeddings of the source code to improve automatic source code generation in Grammatical Evolution. Focusing on a particular programming language, such as Java in the research presented, and being able to represent each Java function in the form of a continuous vector in a linear space by the code2vec model, GE gains some additional knowledge on similarities between constructed functions in the linear space instead of semantic similarities, which are harder to process. We propose a few improvements to the regular GE algorithm, including a code2vec-based initialization of the evolutionary algorithm and a code2vec-based crossover operator. Computational experiments confirm the efficiency of the approach proposed on a few typical benchmarks.
Serra Geral Group Plumbing System and 3D Geological Framework in Southern Paraná-Etendeka Lip (Brazil), Part I: Revealed from Borehole, Geochemical&amp;nbsp;and Petrophysical Data

Morungava region (east of Porto Alegre-RS, Brazil), in the Paraná-Etendeka Large Igneous Province (LIP) southern portion, hosts subvolcanic intrusions of the Serra Geral Group. These intrusions show different compositions, including high-Mg rocks. At the Paraná-Etendeka LIP, only a few examples of high-Mg rocks are known so far. Many studies were carried out in Morungava region, and yet, several questions concerning the subvolcanic bodies distribution, geometry, composition, and structural framework in the subsurface remain unanswered. Therefore, we built a 3D geological model of the Serra Geral Group sills and their associated stratigraphic framework in the Morungava region through the combined analysis of geological (boreholes and geological maps), petrophysical and chemical data to improve the understanding of these bodies underground. The stratigraphic framework represents the Precambrian basement topography, the Paraná Basin units distribution and the Rio Grande do Sul Coastal Plain (RSCP) sediments, that cover most of the studied area. To classify the Serra Geral Group rocks intersected by the boreholes, we developed a methodology of validation and correlation between three different data sources: laboratorial geochemical data, portable X-ray Fluorescence (pXRF) measurements and gamma-ray logs. Through this methodology we categorized the Serra Geral Group rocks in the Morungava region in three types (1, 2, 3), that show compositional similarities with Palmas, Vale do Sol and Torres Formations, respectively, and a fourth type that could be compared to the Lomba Grande complex and the SSOT dikes.
A Conditional Defense of the Use of Algorithms in Criminal Sentencing in advance

The presence of predictive AI has steadily expanded into ever-increasing aspects of civil society. I aim to show that despite reasons for believing the use of such systems is currently problematic, these worries give no indication of their future potential. I argue that the absence of moral limits on how we might manipulate automated systems, together with the likelihood that they are more easily manipulated in the relevant ways than humans, suggests that such systems will eventually outstrip the human ability to make accurate judgments and unbiased predictions. I begin with some reasonable justifications for the use of predictive AI. I then discuss two of the most significant reasons for believing the use of such systems is currently problematic before arguing that neither provides sufficient reason against such systems being superior in the future. In fact, there’s reason to believe they can, in principle, be preferable to human decision makers.
Video Quality Metrics Toolkit: An Open Source Software to Assess Video Quality

Video content on the Internet continues to grow. As a result, streaming platforms must ensure a certain level of quality when preparing their content. To this end, several metrics have been developed to evaluate video quality. This work integrates 14 metrics into a container image to create a cross-platform tool, VQMTK. The tool offers a web interface and a Bash script that combines all metrics into a single tool. Performance tests have demonstrated that the tool is highly efficient and capable of handling all the integrated metrics using 4K video samples. The tool can be used in scientific and educational environments.
The Reasoned Lisper - Bridging Discrete and Continuous Logic in Automated Reasoning Systems

In this paper we will explore the connection between Functional Programming and reasoning. Functional Programming is often advanced as a means to increase type safety, write correct programs, or simply to pursue brevity and succinctness in program design. Much less emphasized, but just as powerful, is the connection between functional programming and logic --- between functional programming and automated reasoning. On our journey we will consider object orientation, relational databases, and a Lisp based system called Powerloom, a Knowledge Representation and Reasoning System that can represent both A) discrete mathematics as well as B) quantitative models such as regression as used in statistics. Powerloom is being developed at the Intelligent Systems Division at the University of Southern California and was funded by DARPA (Defence Advanced Projects Agency). Along the way, we will look at a system called Coq, a theorem prover for OCaml.
udCATS: A Comprehensive Unsupervised Deep Learning Framework for Detecting Collective Anomalies in Time Series

Anomaly detection has recently gained enormous attention from the research community.It is widely applied in many industrial areas, such as information security, financing, banking, and insurance.The data in these fields can mainly be represented as time series data, the corollary being that time series anomaly detection plays an essential role in these applications.Therefore, many authors have tried to solve the problem of collective anomaly detection in time series.They have proposed several approaches, from classical methods such as Isolation Forests to modern deep learning networks such as Autoencoders.However, a comprehensive framework for handling this problem is still lacking.In this work, firstly, we propose using an Attention-based Bidirectional LSTM Autoencoder (Att-BiLSTM-AE) as an anomaly detection model.Furthermore, in the essential part of this paper, we developed a comprehensive unsupervised deep learning framework, udCATS, to solve the problem of detecting collective anomalies in time series.Our experiments show that the Att-BiLSTM-AE outperforms other detection models, and using it within the udCATS framework increases the detection accuracy.
Synthetic Aperture Radar Image Ship Detection Based on YOLO-SARshipNet

Automatic ship detection from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR ship detection methods based on the convolution neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based methods and spatial-domain-based methods exhibit certain limitations. Some algorithms do not consider the detection speed and model scale when improving the detection accuracy, which limits the real-time application and deployment of SAR. To solve this problem, a lightweight, high-speed and high-accurate SAR ship detection method based on yolov3 has been proposed. First, the backbone part of the model is improved, the pure integer quantization network is applied as the core to reduce a small amount of accuracy while reducing the model scale by more than half; Second, modify the feature pyramid network to improve the detection performance of small-scale ships by enhancing feature receptive fields; third, introduce the IoU loss branch to further improve the detection and positioning accuracy; finally, the feature distillation is applied to handle the problem of accuracy decrease caused by model integer quantization. The experimental results on the two public SAR ship datasets show that this algorithm has certain practical significance in the real-time SAR application, and its lightweight parameters are helpful for future FPGA or DSP hardware transplantation.
Radiation Source Identification by Feature Fusion

In order to solve the problem of accurately identifying the emitter in complex environments, time-frequency analysis is performed by Hilbert-Huang transform(HHT), integrated rectangular bispectrum is used to extract features from the received signal. The features extracted by the two methods are fused and a network training is conducted to make final decision. The simulation results show that the emitter individual recognition method based on feature fusion is more stable and has higher recognition accuracy in the case of low signal-to-noise ratio.