bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analyzing Noise Robustness of Cochleogram and Mel Spectrogram Features in Deep Learning Based Speaker Recogntion
Authors: Lambamo Wondimu , Srinivasa Ramasamy , Jifara Worku 
Published: November 2022

Abstract
The performance of speaker recognition is very well in a clean dataset or without mismatch between training and test set. However, the performance is degraded with noise, channel variation, physical and behavioral changes with the speaker. The studies confirmed that the features which represent speech in the Equal Rectangular Band (ERB) scale are more robust than Mel Scale at low Signal to Noise Ratio (SNR) level. Gammatone Frequency Cepstral Coefficient (GFCC) which represents speech in ERB scale is widely used in classical machine learning based speaker recognition at noisy conditions. Recently, deep learning models are widely applied in speaker recognition and show better performance than classical machine learning. Previous deep learning based speaker recognition models used Mel Spectrogram as an input rather than hand crafted features. However, the performance of Mel spectrogram drastically degraded at low SNR level because Mel Spectrogram represents speech in Mel Scale. Cochleogram is another important input to develop deep learning based speaker recognition models. Cochleogram represents speech in ERB scale, which is more robust at low SNR level. However, none of the studies used the Cochleogram feature to develop deep learning based speaker recognition models. In addition, none of the studies conduct analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition using deep learning. In this study, we conducted an analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition by using additive noises (such as: babble, street and restaurant noises).The train-clean-100 part of the LibriSpeech dataset, which consists of 251 speakers (126 male and 125 female speakers) and 28,539 utterances is used for the experiment. CNN model is used for training and classification of speakers into different classes. The evaluation results show that Cochleogram is more robust than Mel Spectrogram at low SNR level. Both Cochleogram and Mel Spectrogram features show approximately equal accuracy at high SNR and without additive noise. In conclusion, the Cochleogram feature improves performance of deep learning based speaker recognition in noisy conditions.
Application of Big Data Analysis and Cloud Computing Technology
Authors: Zhang Jinfang , Li Yajuan , Zhang Chunqian 
Published: November 2022

Abstract
With the swift growth of computer science, technologies such as big data and artificial intelligence are widely used in various fields of modern society. The types of network equipment and the scope of network coverage have also increased rapidly. While the network brings convenience to people, more attention must be paid to the security of the network platform. The purpose is to safely and effectively manage the current rapidly growing Internet data and improve the ability to detect abnormal network behaviors. Combining big data technology and machine learning (ML), the application of big data analysis and cloud computing technology for network security are studied. Firstly, the data collection technology of abnormal network behavior is introduced, and the Flume data collection component and Kafka distributed technology are discussed. Secondly, the data processing process and corresponding algorithm processing of abnormal network behavior are analyzed, including ML framework and stream processing technology. Finally, the model of network abnormal behavior detection based on big data is constructed, and compared with the related model based on the decision tree and random forest (RF) algorithm, and verified by experiments. The verification results reveal that among the 42 attack types against the dataset, the detection accuracy of network abnormal behavior by big data is 96.4%, and the false positive rate is 2.23%, which is higher than that of decision tree and RF algorithm. This experimental study denotes that the network abnormal behavior detection technology of big data based on the ML framework can effectively improve the type and efficiency of network abnormal behavior detection, and has certain reference significance for improving network security management and control capabilities.
Research on Martial Arts Cultural Resource Management Strategy Based on Edge Cloud Computing System
Authors: Xie Xiaoqing , Tan Fei 
Published: November 2022

Abstract
Edge Cloud Computing has only recently begun to gain popularity, there are not many uses for it in China's educational system. The primary characteristic of edge cloud computing is the utilization of virtualization to combine different resources including software and hardware and make them available to consumers online. The implementation of multimedia technology has considerably enriched the curriculum, revitalized the learning environment, and increased the effectiveness of instruction. A significant opportunity for reforming martial arts education is the use of cloud computing technologies in college-level martial arts instruction. This paper primarily examines the demerits in how aerobics is now taught in Chinese colleges and universities, using the teaching of martial arts as an instance. The study has implemented edge cloud computing system for analyzing resource management strategy in martial arts. The strategy's application may lead to some clarity for university martial arts instruction.
Integrating Artificial Intelligence Technology into Ideological and Political Education Innovation by Intelligent Edge Cloud Computing
Authors: Zheng Li 
Published: November 2022

Abstract
The new generation of Artificial Intelligence (AI) technologies, such as big data and natural language processing, have deeply affected all areas of social life. Ideological and political education (IPE) also faces development opportunities and negative impacts. AI should be used for IPE transformation strategies. The main solution to the limitation of educational places is online teaching, but the main problem is how to solve the viewing experience of webcast, and how to assist students in consolidating after-school knowledge. This paper aims to improve the live broadcast through edge cloud computing, and use the automatic marking system to assist students to build an overall knowledge system framework. Firstly, theoretical knowledge such as edge cloud computing, ideological education status, deep learning, Long Short-Term Memory ( LSTM ) and attention mechanism are further sorted out. The key technology of the automatic marking system of ideological and political knowledge points has been deeply studied. This paper focuses on extracting and representing specific information on ideological education subject issues to propose an automatic marking system for ideological and political knowledge points based on deep learning. Real collected data is used for some tests and applications. The test results show that the system has the highest accuracy when the convolution kernel size is 3, and the number is 120. The model's accuracy using the max pooling method is higher than that of the average pooling model. Different models are compared, and the accuracy of the created LSTM-Attention model is relatively higher. In the self-built question bank, the marking accuracy of ideological and political knowledge points reaches 85.2%. This paper hopes to improve the teaching experience of online ideological and political education. The designed automatic labeling system can quickly help students summarize the knowledge they have learned into the existing system to form a framework and alleviate the imbalance of educational resources.
College Physical Education Curriculum and Teaching Management Supported by Edge Cloud Computing and Deep Learning Technology
Authors: Zhao Jinkun , Zhang Liangyu 
Published: November 2022

Abstract
The physical education (PE) curriculum has important practical significance for contemporary college students and should be widely concerned and studied. The college PE curriculum and teaching management should be constantly improved and optimized to meet the diversified needs of college students for the PE curriculum in the new era and make the college PE curriculum a critical part of a college education. The methods of literature, questionnaire, and teaching experiment are adopted. Based on the deep learning technology, this exploration applies the flipped classroom teaching mode to the college PE curriculum, explores the impact of this teaching mode on the teaching effect, and provides a reference for the reform of the PE curriculum and the research of teaching management. Edge cloud computing technology has the advantages of multi-user sharing and resource expansion. Therefore, starting from the resource scheduling and management strategy of edge computing tasks, the research further optimizes the management strategy of physical education courses by migrating mobile data to the cloud data processing center. First, the problems existing in college PE at this stage are explored. Next, the PE curriculum and learners are analyzed to understand the factors affecting deep learning to better optimize and improve the teaching process. Finally, the objectives, contents, environment as well as evaluation of college PE teaching are studied to achieve the purpose of deep learning. The final conclusion shows that the flipped classroom teaching mode on the basis of deep learning has a significant teaching effect. It can enhance students' physical quality, improve their motivation for autonomous learning, and improve their understanding and cognitive level, that is, it can achieve the goal of promoting students' deep learning.
Adaptive Query Compilation in Graph Databases
Authors: Baumstark Alexander , Jibril Muhammad Attahir , Sattler Kai-Uwe 
Published: November 2022

Abstract
Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and to exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the same instructions for processing, this technique can lead to an improvement. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating and optimizing machine code becomes an issue for short-running queries or queries which could produce early results quickly.In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. As soon as the code generation is finished, the execution switches to the compiled code. Our evaluation shows that autonomously switching execution modes helps to hide compilation times and additionally the additional latencies of the underlying storage.
WITHDRAWN: Emotional Brain activity Evaluation in Diabetes mellitus type 2: A case report
Authors: Khalifa Samar 
Published: November 2022

Abstract

Diabetes is one of the major diseases that affects the nervous system of the body either in early or delayed age. The brain activity is affected in some manners during the affection of the body with diabetes as the signals of the blood impulses to the brain can cause unorganized brain activity. This paper reports the activity of the brain during different activities monitoring emotional distress and mood changes on a type 2 diabetes mellitus patient using EEG device and EMOTIV program.
Long-term adaptation and short-term acclimation to high-altitudes in Tibetan Partridges (Perdix hodgsoniae)
Authors: Lamichhaney Sangeet , Palacios Catalina , Wang Pengcheng , Wang Nan , Brown Megan , Capatosto Lukas , Du Juan , Jiang Jiahu , Zhang Qingze , Dahal Nishma 
Published: November 2022

Abstract
Short-term phenotypic plasticity and long-term genetic adaptation are two key evolutionary processes that mediate organismal persistence in a new environment. But generally, there are few vertebrate systems where the molecular architecture of such plastic and genetic changes has been simultaneously characterized. Birds residing across elevational gradients are known to thrive in rapid and long-term environmental stimuli, demonstrate quantifiable plastic and adaptive traits, and thus can be a suitable model system to study molecular mechanisms of phenotypic plasticity and genetic adaptation. Here, we studied three species from the genus Perdix (Phasianidae, Galliformes): Tibetan Partridge (Perdix hodgsoniae), Daurian Partridge (Perdix dauurica), and Grey Partridge (Perdix perdix) by using an integrative approach of population and comparative genomics, transcriptomics, and field-based transplantation experiments. We identified a 60-kilobase haplotype encompassing the ESR1 gene, a ligand-activated transcription factor, showing strong genetic divergence between populations of Tibetan partridges adapted to different altitudes. We identified six SNPs in the ESR1 gene fixed for derived alleles in high-altitude populations that are strongly conserved across vertebrates, which are likely key genetic changes associated with high-altitude adaptation in Tibetan partridge. We also identified strong genetic divergence in additional genes associated with the regulation of apoptosis pathways, possibly indicating genetic adaptation to long-term chronic hypoxia in a high-altitude environment. Transcriptome analysis identified regulation of apoptosis, glycolysis, and response to hypoxia as key pathways among differentially expressed genes between populations of Tibetan partridges residing across the elevational gradient. Similarly, transcriptome analysis in birds transplanted from high to low elevation have identified differential expressed genes related to the immune system, and to stress response (mainly heat). Comparative transcriptomics analyses among Tibetan partridges in their “native” and “transplanted” habitats indicated that there are different suites of genes responding to short-term physiological acclimation or long-term adaptation in this species. However, we found two genes associated with the HIF-1 signaling pathway; Lactate Dehydrogenase-A (LDHA) and Aldolase C (ALDOC) were differentially expressed both (a) across the native populations along the elevational gradient and (b) after experimental transplantation from high to low elevation. Our results highlight Tibetan partridges as a useful study system to explore the genetic architecture associated with short-term acclimation and long-term genetic adaptation to high altitudes.
Diversity and Identification of Aquatic Macrophytes of Four Lakes of Holalkere, Chitradurga District, Karnataka, India.
Authors: MALLAPPA PALLAVI , Takrya Parameswara Naik 
Published: November 2022

Abstract
The present investigation concerns the diversity of aquatic macrophytes in different lakes of Holalkere, Chitradurga district of Karnataka. Lakes are the natural habitat of macrophytic plants. Macrophytes are the common features of an aquatic ecosystem. Aquatic macrophytes play a prominent role in water ecosystems. Aquatic macrophyte diversity and its role in understanding the lake ecosystem dynamics have tremendous significance. Species composition of aquatic macrophytes, seasonal distribution in four lakes in Holalkere viz. Gangasamudra lake, Gowdihalli lake, Talikatte lake and Kudineerakatte lake were studied during 2019–2021. Fifteen different species of aquatic macrophytes were recorded from the studied lakes which include One free-floating, Eight Submerged, and Six Emergent Macrophytes present in the lake basin. Hydrilla verticillate, Polygonum glabrum, Cyperus longus, and Ipomoea fistulosa occur throughout the year. It indicates that aquatic macrophyte species are specific to environmental quality. The present findings revealed that the surface quality of the lakes is productive. The researches aiming to describe unknown species are still necessary.
The quasi-strong solutions of a degenerate electrorheological fluid equation
Authors: Zhan Huashui 
Published: November 2022

Abstract
An electrorheological fluid equation with a convection term is considered.
Based on the existence of weak solutions, the definition of quasi-strong solution is given and its existence is studied. Different from the other related works, it is permissable that there is a point $(x_0,t_0)\in \Omega\times [0,T)$ such that $a(x_0,t_0)=0$. In consideration of the uniqueness of solution, it is even permissable that the set $\{x\in \Omega: a(x,t)=0\}$ has an interior point, the equation may have hyperbolic characteristic, entropy condition should be quoted. Moreover, the boundary value condition is imposed on a submanifold $\Sigma_1\subset\Omega\times (0,T)$, and the submanifold $\Sigma_1$ can not be expressed as a cylinder such as $\Gamma\times (0,T)$, where $\Gamma\subset\partial\Omega$. Based on this partial boundary value condition, the stability of quasi-strong solutions is established, the uniqueness of entropy solution is proved
Bibliometric Mining of Research Directions and Trends for Big Data
Authors: Lundberg Lars 
Published: November 2022

Abstract
In this paper a program and methodology for bibliometric mining of research trends and directions is presented. The method is applied on the research area Big Data for the time period 2012 to 2021, using the Scopus database. It turns out that the 10 most important research directions in Big Data are Machine learning, Deep learning and neural networks, Internet of things, Data mining, Cloud computing, Artificial intelligence, Healthcare, Security and privacy, Review, and Manufacturing. For four geographical regions (North America, European Union, China, and The Rest of the World) we investigate different activity levels in Big Data during different parts of the time period. North America was the most active region during the first part of the time period. During the last years China is the most active region. The citation scores for documents from different regions and from different research directions within Big Data are also compared. North America has the highest average citation score among the geographical regions and the research direction Review has the highest average citation score among the research directions. The program and a methodology for bibliometric mining developed in this study can be used also for other large research areas than Big Data. Now that the program and methodology have been developed, one could probably perform a similar study in some other research area in a couple of days.
Modelling malaria in southernmost provinces of Thailand: a two-step process for analysis of highly right-skewed data with a large proportion of zeros
Authors: Ammatawiyanon Lumpoo , Tongkumchum Phattrawan , Lim Apiradee , McNeil Don 
Published: November 2022

Abstract
Background
Malaria remains a serious health problem in the southern border provinces of Thailand. The issue areas can be identified using an appropriate statistical model. This study aimed to investigate malaria for its spatial occurrence and incidence rate in the southern provinces of Thailand.
Methods
The Thai Office of Disease Prevention and Control, Ministry of Public Health, provided total hospital admissions of malaria cases from 2008 to 2020, which were classified by age, gender, and sub-district of residence. Sixty-two sub-districts were excluded since they had no malaria cases. A logistic model was used to identify spatial occurrence patterns of malaria, and a log-linear regression model was employed to model the incidence rate after eliminating records with zero cases.
Results
The overall occurrence rate was 9.8% and the overall median incidence rate was 4.3 cases per 1,000 population. Malaria occurence peaked at young adults aged 20-29, and subsequently fell with age for both sexes, whereas incidence rate increased with age for both sexes. Malaria occurrence and incidence rates fluctuated; they appeared to be on the decline. The area with the highest malaria occurrence and incidence rate was remarkably similar to the area with the highest number of malaria cases, which were mostly in Yala province's sub-districts bordering Malaysia.
Conclusions
Malaria is a serious problem in forest-covered border areas. The correct policies and strategies should be concentrated in these areas, in order to address this condition.
Sistem Informasi Evaluasi Perkuliahan dengan Sentimen Analisis Menggunakan Naïve Bayes dan Smoothing Laplace
Authors: Ramadhani Nilam , Fajarianto Novan 
Published: November 2022

A good lecture is certainly a goal so that students achieve maximum learning outcomes. In order for good lecture quality, lecture evaluation needs to be done,beside lecturer professional competency training. In order to improve the quality of lectures, Departement Informatics of Madura University (UNIRA) evaluates lecturers' performance in each semester. Form of evaluation is a questionnaire that filled out by students.Results of the questionnaire, then it is analyzed to find out whether the comments are positive, negative, or neutral. The method that can be used to solve the problem of sentiment classification analysis is Naïve Bayes that combined with text processing techniques.The data comments that collected are 342. After grouping the comments by subject, there were 31 comments for subject Human and Computer Interaction (HCI). In this data comments then performed data cleaning, data transformation, text processing and labeling. Then classifying comments using Naïve Bayes with Smoothing Laplace. Results of accuration obtained an accuracy to 80%. The results of implementation Naïve Bayes algorithm with Smoothing Laplace, it can be seen the sentiment analysis of the subjects that lectures taught.
Process Optimization for Biodiesel Production Using Agro-Waste Substrate
Authors:   Savitribai Phule Pune
            University, Ravikumar Ameeta , Bed Rashmi ,   Savitribai Phule Pune
            University, Ravi Kumar V. ,   CSIR-National Chemical
            Laboratory (CSIR-NCL)
Published: November 2022

To address the sustainable energy crisis along with waste management issues we
developed a fungal biomass driven biorefinery based on cheap, abundantly available
agro-waste as input, yielding cleaner burning renewable biodiesel (FAME). MNNG
mutagenized Aspergillus terreus variant, TB21 grown on lignocellulosic substrate,
sugarcane bagasse detoxified liquid hydrolysate supplemented with lipid accumulation
media (DLH-LAM) showed almost 1.5 times higher FAME yields (33.34 mg/g) in contrast to
wild type (24.51 mg/g). To future test the scope for process improvement, media
optimization was carried out by subjecting DLH-LAM components for statistical
optimization by Plackett-Burman design. NH4Cl, yeast extract and trace salt solution
were identified as significant factors enhancing FAME yields to 241.98 mg/g of TB21 wet
biomass at 2% DLH-LAM. Which was 2.8 times higher in comparison to TB21 grown on
un-optimized 3% DLH-LAM, yielding FAME of 86.55 mg/g of wet biomass. Significant
increase in FAME content at comparatively lower carbon requirement improved the
feasibility of the process post media optimization. Our goal is to employ such
strategic optimization techniques at scaled-up level. Preliminary work on this has been
initiated as TB21 variant was grown in 6.7 L Fermenter on 3% glucose-LAM (3L vol), 120
rpm for 72 hours yielding 42.811 g/L wet biomass. Such strategic improvements in
biodiesel generation technology are need of the hour for its environmental and
socioeconomic benefits. Key words: biodiesel; process optimization; in-situ direct
transesterification; biomass; Plackett-Burman; FAME ; Aspergillus terreus