Reviewerly/exHarmony huggingface dataset:
Construction and Application of a Data-Driven Abstract Extraction Model for English Text

In this paper, a single English text is taken as the research object, and the automatic extraction method of text summary is studied using data-driven method. This paper takes a single text as the research object, establishes the connection relationship between article sentences, and proposes a method of automatic extraction of text summary based on graph model and topic model. The method combines the text graph model, complex network theory, and LDA topic model to construct a sentence synthesis scoring function to calculate the text single-sentence weights and output the sentences within the text threshold in descending order as text summaries. The algorithm improves the readability of the text summary while providing enough information for the text summary. In this paper, we propose a BERT-based topic-aware text summarization model based on a neural topic model. The approach uses the potential topic embedding representation encoded by the neural topic model to match with the embedding representation of BERT to guide topic generation to meet the requirements of semantic representation of text and explores topic inference and summary generation jointly in an end-to-end manner through the transformer architecture to capture semantic features while modelling long-range dependencies by a self-attentive mechanism. In this paper, we propose improvements based on pretrained models on both extractive and generative algorithms, making them enhanced for global information memory. Combining the advantages of both algorithms, a new joint model is proposed, which makes it possible to generate summaries that are more consistent with the original topic and have a reduced repetition rate for evenly distributed article information. Comparative experiments were conducted on several datasets and small uniformly distributed private datasets were constructed. In several comparative experiments, the evaluation metrics were improved by up to 2.5 percentage points, proving the effectiveness of the method, and a prototype system for an automatic abstract generation was built to demonstrate the results.
Comparative characteristics of the ability of convolutional neural networks to the concept of transfer learning

The object of research is the ability to combine a previously trained model of a deep neural network of direct propagation with user data when used in problems of determining the class of one object in the image. That is, the processes of transfer learning in convolutional neural networks in classification problems are considered. The conducted researches are based on application of a method of comparison of theoretical and practical results received at training of convolutional neural networks. The main objective of this research is to conduct two different learning processes. Traditional training during which in each epoch of training there is an adjustment of values of all weights of each layer of a network. After that there is a process of training of a neural network on a sample of the data presented by images. The second process is learning using transfer learning methods, when initializing a pre-trained network, the weights of all its layers are «frozen» except for the last fully connected layer. This layer will be replaced by a new one with the number of outputs, which should be equal to the number of classes in the sample. After that, to initialize its parameters by the random values distributed according to the normal law. Then conduct training of such convolutional neural network on the set sample. When the training was conducted, the results were compared. In conclusion, learning from convolutional neural networks using transfer learning techniques can be applied to a variety of classification tasks, ranging from numbers to space objects (stars and quasars). The amount of computer resources spent on research is also quite important. Because not all model of a convolutional neural network can be fully taught without powerful computer systems and a large number of images in the training sample.
Genesis of the Daping Gold Deposit in the Middle Xuefeng Mountain Area, Southern China: Constraints from Geochemistry, Fluid Inclusion, and H-O-S Isotope

The medium-sized Daping gold deposit is located in the middle Xuefeng Mountain area of Southern China with gold ores hosted in sericite phyllite, sericitolite, and mylonite. The auriferous quartz-carbonate-sulfide veins and adjacent alteration rocks are structurally controlled within the NE (northeast) shear zone with NE, NNE (north-east-east), and NW (northwest) trending at high inclination angles. The petrogeochemistry analysis results show that the gold ores are characterized by high content values of SiO2, S, and As and low content values of Al2O3 and Na2O and display strong enrichment of LREE with δEu values ranging from 0.54 to 0.75. Four stages of mineralization/alteration were identified: the first stage has mineral assemblages of quartz+pyrite+arsenopyrite±carbonate minerals, the second stage has mineral assemblages of quartz+polymetallic sulfide minerals (pyrite, arsenopyrite, chalcocite, galena, chalcopyrite, tetrahedrite)±chlorite±carbonate minerals, the third stage has mineral assemblages of quartz and carbonate minerals, and the supergene stage is characterized by limonite±patina which were formed by the oxidation of metal sulfides. Among them, the first stage and the second stage are the main gold mineralization stages. The ore-forming fluid inclusions in quartz are mainly composed of liquid phase (H2O) and gas phase (H2O and CO2), and based on the microthermometric analysis, the first metallogenic stage and second metallogenic stage yielded average homogenization temperature of 184.5 and 255.8°C and average salinity of 7.64 wt.% NaCl eqv. and 11.35 wt.% NaCl eqv., respectively. Thus, the ore-forming fluids belong to H2O-CO2-NaCl, medium-low temperature, and medium-low salinity fluid. The <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>δ</mi> <msub> <mrow> <mtext>D</mtext> </mrow> <mrow> <msub> <mrow> <mtext>H</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mtext>O</mtext> </mrow> </msub> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mi>δ</mi> </mrow> <mrow> <mn>18</mn> </mrow> </msup> <msub> <mrow> <mtext>O</mtext> </mrow> <mrow> <msub> <mrow> <mtext>H</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mtext>O</mtext> </mrow> </msub> </math> values of auriferous quartz are from -51‰ to 62‰ and from -1.44‰ to 5.42‰, respectively, indicating that the ore-forming fluids may belong to mixing fluids of the magmatic fluid and meteoric hydrothermal fluid. The values of δ34S of metal sulfides range from -0.94‰ to 1.98‰ (-0.131‰ in average), implying that sulfur may source from the concealed granite and/or basement metamorphic strata. The Daping gold deposit formed in the Indosinian period under the tectonic environment of compression between the Cathaysian plate and Yangtze plate and may belong to orogenic gold deposits.
Advancement of Image Quality Improvement in Portable Ultrasound Gadgets

Ultrasound technologies have grown popular in the medical field because they are more accurate, however the image quality of hand-held ultrasound devices is comparably low.Thesuggestedmethoduses ConvolutionalNeural Networks to improve the image standard in handheld devices to high visuals. The suggested Convolutional Neural Networks to improve the,image standard in handheld devices ,leading to high visuals. Through histogram equalization, the median filter is used to reduce undesired disturbance and to keep the details while maintaining a high dynamic range. To change the vibrant value's histogram, the graph balancing method is applied.. It spreads out the most frequent pixel intensity values or stretches out the image's intensity range to improve the image contrast. Contrary to what its name suggests, unsharp masking is used to sharpen an image. Sharpening is important when post-processing most digital photos since it helps to emphasis detail.To accomplish higher resolution, a Convolutional Neural Network is often used. CNN was created primarily to handle pixel data. It's a hierarchical model that builds a network, similar to a funnel, and then outputs a fully-connected layer in which all of the neurons are coupled and the outcome is analyzed. By employing CNN, it can provide more accurate training with high accuracy and produce a high-quality reconstruction image with fine details, structure, and speckle.
Efficient Convolutional Neural Network Accelerator Based on Systolic Array

This paper uses 72 PE as the basis for convolution operations, which can handle 3 x 3 and 1 x 1 filter sizes. Moreover, using the Systolic Array design architecture, the data reuse of this architecture is better than general PE architecture. Systolic Array architecture only needs to access once. This paper integrates Convolution and Max Pooling. This hardware verifies on Xilinx ZCU102 FPGA board. The hardware uses quantized weight parameters, and the hardware arithmetic precision is UINT8. The operation frequency sets at 100 MHz, throughput can reach 14.4 GOPs. The efficiency is 98.90&#x0025;, the bandwidth is 150.82 MB, and Convolution integrates Max-Pooling to save 31.75&#x0025; of DRAM access. In the future, the Operation Frequency can increase to more than 200 MHZ. The increase in the number of PEs can enhance the efficiency of parallel operations, which can effectively improve the throughput of the hardware.
Texture Analysis of Corn-based Snacks with Fast Fourier Transform

This paper introduces a computer-based system devoted to process the sound that is produced when a corn-based food product is masticated. The system allows a simple and high-quality sound acquisition eliminating any interference caused by noise and other environmental artifacts. Its user interface displays the collected data in both time and frequency domains. Using the Fast Fourier Transform (FFT), some of the main characteristics related to food texture can be determined. The proposed implementation intends to be a simple yet effective tool that could complement sensory analysis in the food industry by assisting in determining the crunchiness of corn-based snacks, which is often a desirable attribute by consumers.
Tanggung Jawab Pelaku Usaha terhadap Harga Eceran Obat yang Dijual Kepada Konsumen Melebihi Harga Standar

Setting a standard retail price on drug packaging is something that must be done for every drug maker, if the product packaging does not state the standard retail price or when buying and selling takes place does not heed the contents of the relevant law, the government will carry out guidance and supervision of drug maker or seller. The purposes of this study are to reveal the protection of drug consumers who consume drugs whose prices exceed the standard retail price and the responsibility of business actors to the retail prices of drugs sold to consumers that exceed the standard price. This type of research uses a normative legal research type with a case and legislation approach. The sources of legal materials used by the author are primary and secondary legal materials with inventory collection techniques presented in qualitative form. The results of the study reveal that legal protection for consumers against the retail price of drugs sold to consumers exceeds the standard price is divided into two, namely preventive legal protection and repressive legal protection. The responsibility of the drug seller who exceeds the standard retail price is to provide compensation, either in the form of a refund for the purchase of drugs, giving exchange for drugs that have the same use which is equivalent in price. The provision of compensation must be carried out for a maximum of 7 (seven) which starts from the time the sale and purchase agreement occurs.
University Manager – «A Driver with a Navigator at the Wheel of a Good Car». Review of the book «University managers: theory, practice and efficiency of personal work organization»

The review is devoted to the author’s interpretation of the content of the monograph «University managers: theory, practice and efficiency of personal work organization» of S. D. Reznik, I. S. Chemezov (2021). The key subjects of the book devoted to the theory and practice of effective organization of the activities of a modern university manager are revealed. The biographical context of the creation of the monograph and its position in a long-term research and publishing project on the problems of Russian higher education and university management are shown.
Deep Learning for Whole-Brain Cognitive Decoding

Accurately decoding brain activities is both a challenge for machine learning and a potential vehicle for gaining insight into complex cognitive brain states and their dynamics. In this brief note, we will touch upon selected recent directions of our research where machine learning techniques help to analyze brain measurements from EEG, fNIRS and fMRI typically targeting BCI applications. We owe these steps that we are summarizing mainly to activities of members of the BBCI team and their collaborators. Clearly, unavoidably and intentionally this abstract will have a high overlap to prior own contributions as it reports about and discusses these novel ideas and directions.
General style transfer model of wavelet network combined with self-attention mechanism

Artistic style transfer refers to using two images (content image and style image) as a reference, preserving the content of the content image as much as possible and transferring the style characteristics of the style image to content image. Existing methods usually use various normalization techniques, but these techniques have limitations in completely transferring different textures to different spatial locations. The method based on self-attention has solved this problem and made some progress, but there are also unobvious image textures, resulting in unnecessary artifacts. There are also attempts to add a mask to fix the image layout for style transfer, but it is difficult to produce a coordinated result at the mask boundary. Someone tried to embed the wavelet network in the VGG network to obtain more detailed stylized images, and indeed achieved more visually pleasing results. To solve these problems, this paper attempts to combine the advantages of wavelet transform, self-attention mechanism, whitening and coloring transform WCT (Whiten-Color Transform) in image feature extraction, and propose a new general style transfer method to better weigh the semantic information and style characteristics of content images and style images. Moreover, this paper use the self-attention mechanism to obtain the high-level semantic information of the image to make up for the missing details of the reconstructed image. Compared with the previous methods, the proposed method does not need to train for a certain feature map, and any style map can be used to transfer the style of the content map. Experimental results show that the model has good effects in terms of style transfer and artifact removal, and also prove that the method has good versatility.
Chaos and Fractal in Chinese Traditional Music Texts and Its Inheritance

The uncertainty in Chinese traditional music texts has made it possible to inherit the music texts flexibly and creatively. Contemporary information philosophy and complex information system theories reveal the complexity, diversity and ambiguity of human cognition, providing a philosophical and scientific methodology for analyzing complexity issues in different fields. Based on studies on information philosophy and complex information system theories, this paper will discuss Xi&rsquo;an Drum Music texts and the complex information in its inheritance in order to analyze the significance of uncertainty in inheriting Chinese traditional music.
Analysis of E-Learning User Acceptance Factors UPN Veteran Jatim

The utilization of Information and Communication Technology (ICT) has an important role in the field of education during the COVID-19 pandemic. Online learning by utilizing e-learning media is an educational solution for the implementation of learning during pandemics. UPN Veteran Jatim utilizes information and communication technology in teaching and learning by using e-learning (Interactive Learning Management of UPN Jatim). This study aims to analyze the factors that affect user acceptance of UPN Veteran Jatim e-learning . The study used the Unified Theory of Acceptance and Use of Technology (UTAUT) research model. Data sampling was conducted online with a total of 387 respondents. The research data was analyzed using SEM with WarpPLS software. The results showed that performance expectancy, effort expectancy, social influence had a positive and significant effect on students' intentions in using UPN Veteran Jatim e-learning. In addition, facilitating conditions and behavioral intentions also positively affect the behavior of students using UPN Veteran Jatim.
Knowledge Discovery from Library Automation via Bibliomining using the Apriori Algorithm

Today which is called as the digital age with the considerably developing information systems, the constant increase in the data amount being recorded has revealed the concept of big data. Obtaining the strategic information which is crucial for decision-makers especially in managerial terms is only possible through processing these big data with accurate techniques. Data mining techniques have frequently been used in recent years in order to reach meaningful and useful knowledge among data stacks. In this study, the Apriori Algorithm was used for the managers of university library information systems, which provide data-oriented service, to make investment decisions in the future effectively and create user profiles. Within the scope of the study, an application was performed on the basis of an information system comprising of real data of Erzincan Binali Yıldırım University Central Library. By means of association rules, which are one of the descriptive models of data mining, ten different association rules regarding the joint borrowing of publications were applied and results were obtained in the confidence intervals of 57.1% and 95.8%. In addition, information such as library inventory, member profile, and publication borrowing habits were obtained and evaluations were made in line with this information at the end of the study.
Design and Implementation of Multi-Source Information Fusion Integration Platform for Testing and Commissioning of High Speed Railway

The traditional commissioning and testing each system chimney construction, information can't be shared, single point strong and overall weak, lack of professional, professional and external environment between the correlation analysis and centralized fusion of intuitive display. Through the research on key technologies such as multi-source information fusion, cross-platform information interconnection, data sharing, space-time synchronization, multi-graph collaborative linkage, and integrated display, the ‘multi-source information fusion integration platform’ was designed and developed. The application of Beijing-Zhangjiakou and other lines shows that the platform realizes the integration of information fusion among organizations (horizontal), specialties (vertical) and data (end-to-end), and improves the overall judgment, abnormal alarm, collaborative command and scientific decision-making level of testing and commissioning.
Research on architecture design pattern mining of complex information system

This paper analyzes the necessity of applying architecture pattern in complex information system modeling,and first puts forward the definition of architecture mining, based on the process of architecture mining and the method of architecture mining from the aspects of architecture design data mining; and second puts forward how to design architecture pattern template, in the end discusses the role of Architecture pattern in architecture modeling and optimization.
Impact of fracture length distribution on the injection-induced seismicity in fractured rocks

&amp;lt;p&amp;gt;Induced seismicity is a major challenge for fluid injection operations performed by geo-energy industry to exploit the underground resources. Despite recent developments in the understanding of induced earthquakes, many high-pressure fluid injection operations can still trigger unexpectedly large-magnitude events. A physical understanding of geological parameters controlling the induced seismicity is of central importance for improving our ability to forecast and mitigate the risk of inducing large earthquakes.&amp;amp;#160;Current physics-based numerical models are typically based on some simplifications that disregard the multiphysical interactions among fractures and faults. Therefore, the physical linkage between geometrical attributes of the fracture system and the statistics of induced seismicity is poorly understood. The final objective of this research is to determine the impact of fracture network properties on the spatiotemporal evolution of injection-induced seismicity and the emergence of large earthquake events. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we numerically capture the occurrence of seismic and aseismic slips in fracture systems, represented as discrete fracture networks (DFNs), spanning over two orders of magnitude over the length scale (1-100 m). Then, a 2D finite element model is used to simulate the coupled hydraulic and mechanical processes during fluid injection and analyze the occurrence of earthquakes. We present some preliminary results of our numerical simulations based on synthetic fracture network realizations. We particularly focus on power-law exponent of fracture length distribution and analyze the potential controls on the magnitude frequency of induced seismic events. The results of the analysis could have significant implications injection-related activities such as enhanced geothermal systems.&amp;lt;/p&amp;gt;
Power Internet of Things Link Tracking Method Based on Dynamic Sampling Strategy

The sampling strategy of the distributed link tracking system or framework adopted by the existing power Internet of Things has the problem of too much collection and tracking that does not help the normal execution of tasks such as fault analysis. This paper proposes a link tracking method based on a dynamic sampling strategy, The two data structures of sampling strategy tree and execution trajectory graph solve the problem of automatic adjustment of tracking sampling rate and how to quickly and accurately find the service that needs to adjust the tracking sampling rate, and increase the proportion of abnormal execution tracking through the cooperation of the two The experimental results show that the method in this paper effectively increases the proportion of abnormal execution tracking in any time period.