bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disinformation as a Democratic Collective Action Problem or Why a Legal Solution Is Warranted
Authors: Hill Lisa , Douglass Max , Baltutis Ravi 
Published: July 2022

AbstractIn this chapter we explore how false election information violates democratic values; in other words, we examine the extent to which and the manner in which false election information impugns the legitimacy of Australian elections, and in particular, the democratic legitimacy criteria of ‘effective participation’ and ‘enlightened understanding’. These criteria are central pillars of the free speech condition that enables any authentic democracy to function properly. Because there are few incentives to desist from polluting the election information environment and also because of the significant social costs it entails, the problem should be approached as a collective action problem rather than as an issue of individualised rights. This distinction is consistent with jurisprudence on the freedom of political communication implied in the Australian Constitution and endorsed in multiple judgements, as we show in detail in Chapter 5. We conclude this chapter by arguing that compulsory voting places an extra duty on the Australian state to ensure that voting takes place in a relatively clean information environment.
Current Approaches to Combatting the Emerging ‘Infodemic’
Authors: Hill Lisa , Douglass Max , Baltutis Ravi 
Published: July 2022

AbstractIn this chapter we examine how the false election information problem has been managed so far in Australia. Up to this point, mainly non-legislative means, such as fact-checking services, voluntary codes of conduct and civil society remedies have been used. Defamation laws have also been invoked to address the problem and we assess their capacity to perform this function. Although there is value in these alternative methods, we question whether they will be sufficient to adequately combat the problem, particularly in the digital age. We conclude that they should be used in conjunction with the legal solution we propose.
Why Australia Is a Great Place to Start: The Implied Freedom of Political Communication and TIPA Laws
Authors: Hill Lisa , Douglass Max , Baltutis Ravi 
Published: July 2022

AbstractIn this chapter we show that the Australian constitutional and jurisprudential approach to political speech, as embodied in the implied freedom of political communication, makes Australia a uniquely congenial setting for TIPA laws and the type of burden they place on political speech. This is reflected in a range of High Court decisions as well as the fact that the Supreme Court of South Australia has upheld the constitutionality of the South Australian TIPA law based on Australian implied freedom of communication jurisprudence. Our exploration of these decisions and their broader context throws light on how such a freedom is supposed to work and is constituted. Notably, the Courts have achieved a balance in the ‘freedom-fairness’ trade-off, and indicated that they consider TIPA laws, in constraining some political speech, as tipping the scales towards ‘fairness’ without at the same time unduly impacting freedom. We end the chapter by summarising our argument so far.
Implementation of s 113: Lessons to Adopt, Pitfalls to Avoid and Refinements to Pursue
Authors: Hill Lisa , Douglass Max , Baltutis Ravi 
Published: July 2022

AbstractBecause s 113 has been in place for some time it has much to teach us about how to design a viable regime for truth in election advertising. However, in sketching out our preferred or ideal model we offer a number of enhancing modifications to SA’s framework, some of which are inspired by practice (and shortcomings) in other common law jurisdictions. We focus here on the implementation of s 113, in particular on issues associated with: whether the publication of misleading election information should be a civil or criminal matter; timeliness and resources including ergonomic aspects of the investigation process; the notion of ‘material extent’ and its complications in determining a breach of s 113; the issue of possible unintended consequences of TIPA-type legislation; problems associated with determining the difference between purported statements of fact and opinion; legal defences; and appropriate penalties and adjudicators.
Application of Image Recognition in Precise Inoculation Control System of Pleurotus Eryngii
Authors: Meng Xiangxiu , Zhu Xuejun , Ding Yunpeng , Qi Dengrong 
Published: July 2022

AbstractThe traditional inoculation technology of Pleurotus eryngii is artificial inoculation, which has the disadvantages of low efficiency and high failure rate. In order to solve this problem, it is necessary to put forward the automatic control system of Pleurotus eryngii inoculation. In this paper, based on the system of high reliability, high efficiency, flexible configuration and other performance requirements, PLC is used as the core components of the control system and control the operation of the whole system. In order to improve the efficiency of the control system, the particle swarm optimization algorithm was used to optimize the interpolation time of the trajectory of the manipulator. Through simulation, it was found that the joint acceleration curve was smooth without mutation, and the running time was short. Because the position deviation of the Culture medium of Pleurotus eryngii to be inoculated will inevitably occur when it is transferred on the conveyor belt, the image recognition technology is used to accurately locate them. In order to improve the efficiency of image recognition, the genetic algorithm (GA) is used to improve Otsu to find the target region of Culture medium of Pleurotus eryngii to be inoculated, and the simulation results showed that the computational efficiency could be increased by 70%. In order to locate the center of the target region, the mean value method is used to find their centroid coordinates. At last, it is found by simulation that the centroid coordinates could be accurately calculated for a basket of 12 Pleuroides eryngii medium to be inoculated.
A Novel Robust Adaptive Color Image Watermarking Scheme Based on Artificial Bee Colony
Authors: Xiao Tingting , Li Wanshe 
Published: July 2022

AbstractThis paper proposes a new robust adaptive watermarking scheme based on dual-tree quaternion wavelet and artificial bee colony, wherein the host images and watermark images are both color images. Color host images and watermark images in RGB space are transformed into YCbCr space. Then, apply Arnold chaotic map on their luminance components and use the artificial bee colony optimization algorithm to generate embedding watermark strength factor. Dual-tree quaternion wavelet transform is performed on the luminance component of the scrambled host image. Apply singular value decomposition on its low-frequency amplitude sub-band to obtain the principal component (PC). Embed the watermark into the principal component. Analysis and experimental results show that the proposed scheme is better as compared to the RDWT-SVD scheme and the QWT-DCT scheme.
Detection and Location of Myocardial Infarction from Electrocardiogram Signals Using Median Complexes and Convolutional Neural Networks
Authors: Liu Shijie , Bin Guanghong , Wu Shuicai , Zhou Zhuhuang , Bin Guangyu 
Published: July 2022

AbstractWhen doctors judge myocardial infarction (MI), they often introduce 12 leads as the basis for judgment. However, the repetitive labeling of nonlinear ECG signals is time-consuming and laborious. There is a need of computer-aided techniques for automatic ECG signal analysis. In this paper, we proposed a new method based on median complexes and convolutional neural networks (CNNs) for MI detection and location. Median complexes were extracted which retained the morphological features of MIs. Then, the CNN was used to determine whether each lead presented MI characteristics. Finally, the information of the 12 leads was synthesized to realize the location of MIs. Six types of MI recognition were performed, including inferior, lateral, anterolateral, anterior, and anteroseptal MIs, and non-MI. We investigated cross-database performance for MI detection and location by the proposed method, with the CNN models trained on a local database and validated by the open PTB database. Experimental results showed that the proposed method yielded F1 scores of 84.6% and 80.4% for the local and PTB test datasets, respectively. The proposed method outperformed the traditional hand-crafted method. With satisfying cross-database and generalization performance, the proposed CNN method may be used as a new method for improved MI detection and location in ECG signals.
Experimental Performance Analysis of Machine Learning Algorithms
Authors: Khekare Ganesh , Turukmane Anil V. , Dhule Chetan , Sharma Pooja , Kumar Bramhane Lokesh 
Published: July 2022

AbstractMachine Learning models and algorithms have become quite common these days. Deep Learning and Machine Learning algorithms are utilized in various projects, and now, it has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the Stock Price Prediction system, it is used to identify the future asset prediction of a industry or other financial aspects traded on a related transaction. Now, it is a daunting task to find the right algorithm or model for such a purpose that can predict accurate values. There are several other systems such as recommendation systems, sales prediction of a mega-store, or predicting what are the chances of a driver meeting an accident based on his past records and the road they’ve taken. These problem statements require to be built using the most suitable algorithm and identifying them is a necessary task. This is what the system does, it compares a set of machine learning algorithms while determining the appropriate algorithm for the selected predictive system using the required data sets. The objective is to develop an interface that can be used to display the result matrix of different machine learning algorithms after being exposed to different datasets with different features. Besides that, one can determine the most suitable (or optimal) models for their operations, using these fundamentals. For experimental performance analysis several technologies and tools are used including Python, Django, Jupyter Notebook, Machine Learning, Data Science methodologies, etc. The comparative performance analysis of best known five time series forecasting machine learning algorithms viz. linear regression, K – nearest neighbor, Auto ARIMA, Prophet, and Support Vector Machine is done. Stock market, earth and sales forecasting data is used for analysis.
Performance Analysis of Fault Detection Rate in SRGM
Authors: Sun Zhichao , Zhang Ce , Wen Yafei , Fan Miaomiao , Liu Kaiwei , Li Wenyu 
Published: July 2022

AbstractThe fault detection rate is one of the main parameters of the software reliability model. Different forms of fault detection rates have different functions. This paper focuses on the influence of fault detection rate on software reliability, proposes a single reliability model multi-failure data set multi-fault detection rate analysis plan, and analyses the impact of fault detection rate on SRGM. After experimental analysis, the performance of the software reliability model corresponding to the power function and the S-type fault detection rate is better, the performance of the software reliability model corresponding to the constant fault detection rate is acceptable, and the comprehensive performance of the software reliability model corresponding to the exponential fault detection rate is poor. The research in this paper has a certain guiding role in the selection of parameter models in software reliability modelling and the determination of the optimal release time.
Research on Vibration Index of IRI Detection Based on Smart Phone
Authors: Zeng Jingxiang , Zhang Jinxi , Cao Qianqian , Guo Wangda 
Published: July 2022

AbstractWith the development of science and technology, intelligent pavement smoothness detection becomes possible. Intelligent IRI (International Roughness Index) detection is one of the important development directions of pavement performance detection. Different from traditional IRI detection, intelligent IRI detection uses smart phones to collect traffic vibration data. There are many vibration indexes in IRI evaluation unit of driving vibration data, and IRI evaluation can be realized by extracting vibration indexes. In this study, the corresponding relationship between pavement vibration data and IRI is preliminarily proved by driving test. The synthetic vibration acceleration index can reflect the change of IRI. The length of IRI evaluation unit reflects different significance of pavement performance, and the evaluation vibration index extracted is different. When the evaluation unit is short, IRI reflects the local pavement performance of the evaluation unit, and the correlation between the minimum value of vehicle synthetic vibration acceleration and IRI is the best. When the evaluation unit is long, IRI reflects the overall pavement performance of the evaluation unit, and the correlation between the average value of the absolute value of the vehicle synthetic vibration acceleration and IRI is the best.
Research on Universities’ Control of Online Discourse Power in the Period of COVID-19: A Case Study of Shanghai Universities
Authors: Sun Lei , Fu Zhuojing 
Published: July 2022

AbstractUnder the situation of the normalization of the prevention and control of COVID-19, related online public opinion occurs from time to time. University administrators must grasp the right of online discourse to guide the direction of online public opinion and ensure the stability of campus order. This paper analyzes the necessity and feasibility of university administrators to grasp the right of online discourse from the basis of reality, compares two kinds of measures and their combinations through questionnaires and computer simulation experiments: publishing authoritative information and focusing on opinion leaders, argues the effectiveness of these two types of measures, and puts forward specific countermeasure suggestions on this basis.
An Insight into Load Balancing in Cloud Computing
Authors: Tasneem Rayeesa , Jabbar M. A. 
Published: July 2022

AbstractCloud Computing has emerged as a High-performance computing model providing on-demand computing resources as services via the Internet. Services include applications, storage, processing power, allocation of resources and many more. It is a pay-per-use model. Despite of providing various services, it is also experiencing numerous challenges like data security, optimized resource utilization, performance management, cost management, Cloud migration and many more. Among all, Load Balancing is another key challenge faced by Cloud. Effective load balancing mechanism will optimize the utilization of resources and improve the cloud performance. Load balancing is a mechanism to identify the overloaded and under loaded nodes and then balance the load by uniformly distributing the workload among the nodes. Various load balancing mechanisms are proposed by various researchers by taking different performance metrics. However existing load balancing algorithms are suffering from various drawbacks. This paper emphasizes the comparative review of various algorithms on Load Balancing along with their advantages, shortcomings and mathematical models.
A Multi-modal Time Series Intelligent Prediction Model
Authors: Xian Qingyu , Liang Wenxuan 
Published: July 2022

AbstractThe power load prediction can ensure the power supply and dispatch, which will be useful for market participants to plan and make strategic decisions to enhance reliability, save operation and maintenance costs. Short-term load data series have obvious approximate periodicity, while long-term load data series show variability and dynamic features. In addition, time series data of various modalities, such as market reports and production management data, could play a role in load prediction. One kind of multi-modal CNN-BiLSTM architecture is proposed to predict short-term and long-term load data, which have an improved shared parameter convolutional network to learn feature representation and an improved attention-based BiLSTM mechanism, which could model the dynamic features of multimodal on time series data. Experimental results on multimodal dataset show that, compared with other baseline systems, this model has some advantages in the prediction accuracy.
Research on the Deployment Strategy of Enterprise-Level JCOS Cloud Platform
Authors: Jiang Jianfeng , An Shumei 
Published: July 2022

AbstractRuijie JCOS cloud management platform is the first cloud management platform based on OpenStack principle in China. It has the advantages of stable operation, fast deployment, wide compatibility and high performance. Taking the basic technology of cloud platform management as the core, this paper gives a general description of the deploy of the whole cloud platform, from which we can understand and analyze the shortcomings of building traditional data center, and then illustrate the general process of integrating resources and reducing costs by virtualization technology in combination with real application practice.
A Private Cloud Platform Supporting Chinese Software and Hardware
Authors: Li Man , Wang Zhiqiang , Zhao Jinyang , Ou Haiwen , Chi Yaping 
Published: July 2022

AbstractThis paper designs and implements a private cloud platform deployed on an office system that supports domestic software and hardware. With the rapid development of cloud computing, more and more enterprises and users choose cloud platform as a vital Internet resource. At present, most private cloud technologies rely on mature foreign commercial applications and frameworks, and it isn’t easy to achieve compatibility between Chinese software and hardware. Therefore, it is urgent to design a private cloud platform that supports Chinese software and hardware. The key private cloud technology of the cloud platform designed in this paper is the key technology of private cloud that supports independent and controllable Chinese software and hardware. The cloud platform uses virtual computing, virtual storage, virtual network, and other technologies to complete the virtualization of computing resources, storage resources, and network resources. Users can centrally schedule and manage virtual resources.
Gait Planning of a Quadruped Walking Mechanism Based on Adams
Authors: Gao Gangyi , Ling Hao , Ou Cuixia 
Published: July 2022

AbstractIn this paper, the kinematics analysis and gait planning of quadruped walking mechanism are carried out. Firstly, a simplified four-legged mechanism model is established; then the kinematics of the walking mechanism is analyzed; On this basis, the gait planning of walking mechanism is studied, the forward motion (four step movement) gait is analyzed, the corresponding leg swing order of each gait is calculated; Finally, ADAMS software is used to simulate and analyze the gait planning.
Design and Implementation of Full Adder Circuit Based on Memristor
Authors: Tang Ning , Wang Lei , Xia Tian , Wu Weidong 
Published: July 2022

AbstractIn order to break through the traditional von Neumann architecture of computing and memory cell separation and speed up the computing speed, it is necessary to realize in memory computing, and memristor is an excellent carrier to realize in memory computing. Then, the development, principle, characteristics and application prospect of memristor are briefly introduced, and the characteristic curve of memristor is obtained by simulating the model of memristor. The principle and characteristics of memristor are explained more intuitively. Then, based on the memory resistor, the simple logic circuit design principle is described. The logic structure can be realized by using the memory resistor as the calculation element and adding a CMOS inverter, so as to realize the simple logic circuit. The paper designs the simple logic circuit including gate, gate, or gate by spice software, and simulates the circuit of gate, gate, gate, or gate. Then, based on the above logic gate, the circuit design of adder is carried out, the circuit diagram and design scheme are given, and the simple description and SPICE simulation are given. The design scheme is reviewed and summarized, its advantages and disadvantages are analyzed, and the optimization and improvement scheme is proposed.
Design of Intelligent Recognization System Architecture Based on Edge Computing Technology
Authors: Guo Lejiang , Xiao Lei , Chen Fangxin , Tu Wenjie 
Published: July 2022

AbstractWith the increasing acceleration of 5G network construction, artificial intelligence, Quantum technology, edge computing provides content distribution and storage computing services near the network edge which greatly reducing the delay of data processing and service delivery. Starting with the process of information support and decision planning, it analyzes the relationship between edge computing, Quantum and the massive military data. It puts forward an intelligence system architecture design based on edge computing and Quantum. Combined with the openness and flexibility of the system architecture, this paper realizes the mix between data platform and data. It realizes the connection with the existing intelligence system which improves the efficiency of existing data and expands the scenario of edge computing.
Multi-objective Reliability Optimization of a Pharmaceutical Plant by NSGA-II
Authors: Chebouba Billal Nazim , Mellal Mohamed Arezki , Adjerid Smail 
Published: July 2022

AbstractThis work addresses the use of a MO optimization algorithm to deal with the reliability optimization problem in order to determine the redundancy and reliability of each component in the system. Often, these problems are formulated as a single-objective problem with mixed variables (real-integer) and is subject to various design constraints. Classical solution approaches were limited to deal with these problems and most recent solution approaches are based on nature-inspired optimization algorithms which belong to artificial intelligence (AI). In the present paper, the problem is solved as a MO optimization problem through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate the set of optimal solutions, also called Pareto. The latter helps the decision-maker. The case studied consists of a pharmaceutical plant.