Reviewerly/exHarmony huggingface dataset:
Handwritten Digit Recognition Based On Spiking-Vovnet and Extended Application

Abstract Spike Neural Network (SNN) can provide an efficient reasoning process. In this paper, by modifying the Vovnet network to use IF neurons to replace the traditional Vovnet RELU neurons, testing on the open handwritten digit mnist data set, and comparing the Vovnet network structure of CNN and SNN for comparative experiments, it is found that the Spiking-Vovnet network is guaranteed and traditional Under the premise of the accuracy of the Vovnet network, the speed has been greatly improved, and at the same time, the resource consumption is reduced. In order to expand the application, in the synthetic aperture radar (SAR) image target detection, the Spiking-Vovnet network is used as the FCOS ship target detection feature extraction network. For promote the prise degree of detection, the variability convolutional neural network is lead into design a SAR ship Feature extraction network for ship target detection.
Exploration and Practice of "1331" University Integrated Classroom Teaching Mode

Classroom teaching is the main battlefield of school education. Improving the quality of classroom teaching is the fundamental and guarantee for improving the quality of university teaching. Through the exploration and practice of the "1331" university integrated classroom teaching model, learning as the center, constantly innovating the teaching concepts of teachers and students, fully integrating online and offline teaching, implementing the evaluation of the whole learning process, and creating online and offline, intra- and extra-curricular integrated university funding Classrooms enable students to comprehensively improve their knowledge, abilities and qualities, and play an important role in improving the quality of talent training.
Global Feature Representation Using Squeeze, Excite, and Aggregation Networks (SEANet)

Convolutional neural networks (CNNs) are workhorses of deep learning. A popular architecture in CNN is Residual Net (ResNet) that emphasizes on learning a residual mapping rather than directly fit input to output. Subsequent to ResNet, Squeeze and Excitation Network (SENet) introduced a squeeze and excitation block (SE block) on every residual mapping of ResNet to improve its performance. The SE block quantifies the importance of each feature map and weights them accordingly. In this work, we propose a new architecture SEANet built over SENet by introducing an aggregate block after SE block. We choose sum as the aggregate operation. The aggregation helps in minimizing redundancies in feature representation and provide a global feature representation across feature maps by downsampling their number. We demonstrate the superior performance of our SEANet over ResNet and SENet on benchmark CIFAR-10 and CIFAR-100 datasets. Specifically, SEANet reduces classification error rate on CIFAR-10 by around 2% and 3%, respectively, over ResNet and SENet. On CIFAR-100, SEANet reduces error by around 5% and 9% when compared against ResNet and SENet. Further, SEANet outperforms the latest EncapNet and both its variants EncapNet+ and EncapNet++ on CIFAR-100 dataset by around 2%.
Relationship between Emotional Intelligence with Verbal Aggressiveness in Street Children.

Verbal aggressiveness is a behavior that has a purpose to hurt other people’s feeling by saying harsh words, insulting, mocking, and threatening someone. Factor that underlie the emergence of verbal aggressiveness in street children, one of them is emotional intelligence. Emotional intelligence is ability to control oneself, have endurance when faced with a problem,self motivated, able to set the meed, ability to empathize and build relationships with others. The research mainly happen to understand relation between emotional intelligence and verbal aggressiveness in street children. This research method using quantitative approach. Subjects are picked by Incidental sampling techniques and involving 40 street children. Emotional intelligence scale is picked as collecting tool and also verbal aggressiveness scale. Data analysis uses Shpearman’s Rho technique. The result on Shpearman’s Rho correlation technique shows correlation (r) = -0,721 at a significant level (p) = 0,000 (p&lt;0,05). The conclusion is there is a negative and significant correlation of the relationship between emotional intelligence with verbal aggressiveness in street children. So, if someone has low emotional intelligence, then verbal aggressiveness will be high.
Research on Burst Terms Detection Based on Entropy Weight Method

Since burst terms are forward-looking and informative, burst term detection (BTD) helps to predict emerging research trends in certain subject areas. However, currently several BTD models are short of simple operation and objective identification. In this paper, we propose a burst term detection model based on entropy weight (entropy-BTD). Under the model, terms were classified into various burst levels by clustering entropy-variation, and the calculation formula of burst comprehensive value was deduced. Experiments showed that three burst classes were identified from clustering: sudden-burst terms, strong-burst terms, and weak-burst terms. The burst terms with high-credibility burst and positive trend were deleted.
Data Accountability and Security Enhancement in Remote Healthcare System Using BaaS

With the current situation of corona pandemic, the roots of all hospital managements have been shaken, which has resulted in a need for a remote healthcare system. This can reduce the physical presence of patients and give a common and safe platform for everyone. Considering such a remote healthcare system, there are two major setbacks which need to be addressed, viz. Accountability and Security. In this proposed model, we are addressing both the issues using Blockchain as a Service. Here, the service provides additional functionality to the remote healthcare system without interfering in its operations. Further by including service, no one can delete or tamper the data resolving both the issues. An additional feature of Blockchain as a Service is that the cost of achieving these features gets reduced as this service can be integrated with multiple systems without its development and maintenance costs. Accountability refers to the ownership of data, in which once the data recorded for any user cannot be denied by him any time after that. Security here refers to data security in which data tampering any data loss is avoided. Implementing Blockchain here enhances the data security of the application.
Methodology of Performance of Objectives and Tasks in Forming a Culture of Life Safety

Revealing the qualities and properties of FOLSC objects, as well as the methods and tools that affect these qualities and characteristics, the formation of a culture of public safety is one of the most effective tools for managing the worldview of a recognized person and society. The radical solution to this problem is to educate themselves in a safe way so that they can ensure their own safety at a higher level than that of others, at a level of advanced spirituality, skills, and ability.
Application of Deep Learning For Sentiment Analysis

Deep learning is a type of artificial intelligence that employs neural networks, a multi-layered structure of algorithms. Deep learning is an accumulation of artificial intelligence statistics based on artificial neural networks for the teaching of functional hierarchies. In sentiment analysis, deep learning is also applied. This paper begins with an overview of deep learning before moving on to a detailed examination of its present uses in sentiment analysis.
Leveraging full-text article exploration for citation analysis

Abstract Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.
Research and analysis of Computer Modernization in the Integration of Chinese Education in Education of Chinese Characters in Southeast Asia

Abstract With the development of global economic integration, Chinese language education has attracted more and more overseas scholars, and we also put forward higher requirements for Chinese character education. In this paper, the general situation of the teaching of Chinese as a foreign language and the deficiencies of the current teaching of Chinese as a foreign language are explained, and combined with the Chinese education in Southeast Asian countries computer education Chinese teaching modernization development to discuss, for the reference of readers.
Green IT: Energy Efficient Constructions and Applications for Data Centers and Clusters

This paper is devoted to an actual concept of “Green IT” with the background of the “hardcore” resource consuming applications. The given work discusses the energy efficient constructions for computing centres and clusters, which provide optimisation of performance as well as PW, PUE and ERE factors. The following both approaches are examined: 1) workload consolidation and intelligent VM migration; 2) optimisation of the cooling process (guaranteeing PUE) and energy recycling (guaranteeing ERE and CO\(_2\) footprint reduction). The optimal ERE within the data centres and computing clusters provides advanced resource-consuming distributed applications like pharmacology simulations, Big Data analytics, Blockchain, crypto-currency mining.
Abnormal user identification in online social networks based on user behavior

Recently, in online social networks zombie fans have been shown a more complex and humanizing form. The existing methods based on basic features such as the number of followee, follower scale, user name and information content are not very efficient, which may lead to many misrecognitions and missed detection. Behavior pattern is the most fundamental feature of online users and abnormal users certainly have particular actions which are different from the normal users. Therefore, in this paper, a zombie fans identification method has been proposed based on the behavior characteristics like retweet, comment and the corresponding regularity. Furthermore, with user behaviors, invalid user identification is also researched. The experimental results showed that the abnormal user recognition method proposed in this paper had high identification accuracy.
Research on Road Pothole Detection Method Based on Computer Image Restoration Technology

Abstract The detection of road potholes plays an important role in understanding the road damage, formulating and evaluating maintenance strategies. The traditional road maintenance tech is mainly based on manual visual inspection, which has low efficiency, long cycle and high labor cost, which is not conducive to large-scale road maintenance. Based on this, this paper first analyzes the mathematical model of road pothole detection based on image restoration tech, and then studies the road pothole detection method and model construction based on computer image restoration tech.
Userspace Software Integrity Measurement

Todays computing systems are more interconnected and sophisticated than ever before. Especially in healthcare 4.0, services and infrastructures rely on cyber-physical systemss (CPSess) and Internet of Things (IoT) devices. This adds to the complexity of these highly connected systems and their manageability. Even worse, the variety of emerging cyber attacks is becoming more severe and sophisticated, making healthcare one of the most important sectors with major security risks. The development of appropriate countermeasures constitutes one of the most complex and difficult challenges in cyber security research. Research areas include, among others, anomaly detection, network security, multi-layer event detection, cyber resiliency, and integrity protection.
Property Ownership and the Legal Personhood of Artificial Intelligence

This paper adds to the discussion on the legal personhood of artificial intelligence by focusing on one area not covered by previous works on the subject – ownership of property. The author discusses the nexus between property ownership and legal personhood. The paper explains the prevailing misconceptions about the requirements of rights or duties in legal personhood, and discusses the potential for conferring rights or imposing obligations on weak and strong AI. While scholars have discussed AI owning real property and copyright, there has been limited discussion on the nexus of AI property ownership and legal personhood. The paper discusses the right to own property and the obligations of property ownership in nonhumans, and applying it to AI. The paper concludes that the law may grant property ownership and legal personhood to weak AI, but not to strong AI.
The Simulation Model of Collective Management Decision-Making Implemented in Netlogo

The model developed by M. Cohen, J. March, and J. Olsen, which describes the decision-making in an organization based on event flows, has a wide range of applicability. However, despite the presence of a large number of works that develop the idea of the model or modify its algorithm, there is a fairly widespread point of view that treats the collective decision-making process described by the model as random movements of managers and problems to be solved according to the available choices (abstractions of committees and meetings). This ideal image is applied to the study of the processes of making both political and managerial decisions. This point of view is erroneous, in our opinion: the structure of the system of decision-making determines the behavior of managers and the effectiveness of the whole system in the most significant way. Thus, in the presence of a significant number of works that deepen the analysis of the original model (including those published in the last decade), we can state the absence of a scientific tool that allows us to visually reproduce the results of the processes of decision-making taking place in the systems of decision-making with different structures. The need to develop such a tool determines the novelty of this work. The purpose of the study is to develop a simulation model that reproduces the logic of the original model and allows to present the results of the work of the decision-making system visually. The initial data of the study were the logic and algorithm of the original model, our development tool is the NetLogo simulation environment. The developed scientific tool – a simulation model of decision-making – allows us to visually present the results of the work of the decision-making system for various types of structure, determined by the combination of the type of access structure and decision structure to the choice opportunity. The model allows us to visualize the number of solved and unsolved problems, the state of the choice possibilities at the time of the simulation experiment, the types of the solutions developed by the system, the amount of energy required to solve the problems that entered the system, and the amount of effective energy of managers that was not used in the decision-making process. The model code is available for download and modification. It can be used both in the educational process and in the study of decision-making processes in various types of organizational structures.
An Extensive Evolutional Survey of Medical Domain Data Analytics &amp;amp; Decision Improvisation Systems

Medical Domain is a massive and vast set of recorded data which stand to be very important in every geographical region of the world's population. This record sets constitutes Electronic & Hand-Written Health Records (EHR), Public domain Individual's Hospital Information (HIS), information from Radiology Systems (RIS), Information from Laboratory (LIS), information from Clinical Data etc. Old Systems conveys a traditional approach to determine patients' health requirements, progress. With introduction of Artificial Intelligence technologies, these decisions are electronically predicted, and most times found these decisions are approximated to its appropriation. In this paper, we surveyed extensively the old medical systems records analysis & decision approaches, modern artificial intelligence's technology evolutions and its improvisation study towards the approximation to appropriations.
Decoding the Molecular Subtypes of Breast Cancer Seen on Multimodal Ultrasound Images Using an Assembled Convolutional Neural Network Model: A Prospective and Multicenter Study

Background: Preoperatively determining breast cancer molecular subtypes can facilitate individualized treatment plan-making and improve patient prognosis. We aim to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images.Methods and Materials: This multicenter study prospectively evaluated a dataset of grayscale US, color Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancers were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on grayscale US images, the dual-modal ACNN model based on grayscale US and CDFI images, and the multimodal ACNN model based on grayscale US and CDFI and SWE images were constructed in the training cohort. The performances of the three ACNN models in predicting four-classification molecular subtypes and identifying triple negative from non-triple negative were assessed and compared in the internal validation and two independent test cohorts.Findings: The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89-0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81-0.84) and monomodal ACNN model (macroaverage AUC: 0.73-0.75) in predicting four-classification molecular subtypes. The multimodal ACNN model also outperformed the other two ACNN models in identifying triple-negative from non-triple-negative breast cancers (AUC: 0.934-0.970 vs. 0.688-0.830 vs. 0.536-0.650, P <0.05). Even T1 lesions also obtained satisfactory prediction performance (AUC: 0.957-0.958 vs. 0.932-0.985, P >0.05, in comparison to non-T1 lesions).Interpretation: The multimodal ACNN model can preoperatively predict breast cancer molecular subtypes and assist clinicians in treatment plan-making.Funding: This work was supported in part by the National Natural Science Foundation of China (Grants 81671695, 81725008, and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200 and 19DZ2251100).Declaration of Interest: The authors report no conflicts of interest.Ethical Approval: This multicenter study was approved by the institutional Clinical Research Ethics Committee of the three participating centers, and the informed consent was obtained from all patients. Prospective research of this study was registered at www.chictr.org.cn (ChiCTR2000038606).
Overcoming metric diversity in meta-analysis for software engineering: proposed approach and a case study on its usage on the effects of software reuse

This work addresses the problem of metric diversity in meta-analysis for Software Engineering by clustering studies using input-output tables and by vote-counting. Diversity arises when researchers, measuring same phenomena, use different, and typically "incomparable" metrics making impossible a direct analysis of the effects and their sizes. Additionally, this work discusses an application of proposed approach to the case of Software Reuse.
Research of Expressway Vehicle Detection Based on Faster R-CNN and Domain Adaptation

For the traditional object detection model affected by weather, illumination and occlusion factors, the application scene is single, high missing rate of small vehicle and poor detection results problems in the expressway scene, this paper put forward an improved domain adaptive Faster R-CNN algorithm, by adding image-level, instance-level domain classifier, consistency loss component, and improvement RPN network. Adopting multi-scale training during the training and using mining the hard samples fine tuning the train model. The improved model can get the gain by 4.8%. The experimental results show that the adaptive domain is effective to deal with the domain transfer between different domains, and the improved model can significantly raise the detection performance of small-scale object detection. It is proved that the improved method effectively enhance the accuracy and robustness of the model, and also has certain generalization ability.
A Development Scheme of Simulation Training Software

In order to enable a certain instrument operator to receive as much training as possible during the training process, at the same time, reducing the use loss of equipment, it is planned to develop a set of simulation training software to achieve the above purpose. The software has three functions: one is to complete normal exercise functions, the second is the ability to test and score, the third is to be able to conduct operation guidance. Based on the above ideas, carry out the overall plan design of the software in the early stage. By designing the overall development plan of the software, standardize the specific implementation of the software, clarify the functional requirements that the software should meet, and lay the foundation for subsequent software development.
EvoMLP: A Framework for Evolving Multilayer Perceptrons

Designing neural networks for classification or regression can be considered a search problem, and, as such, can be approached using different optimization procedures, all of them with several design challenges: The first and more important is to constrain the search space in such a way that proper solutions can be found in a reasonable amount of time; the second is to take into account that, depending on how the optimization procedure is formulated, the fitness score used for it can have a certain degree of uncertainty. This means that creating a framework for evolving neural networks for classification implies taking a series of decisions that range from the purely technical to the algorithmic at different levels: neural or the optimization framework chosen. This will be the focus of this paper, where we will introduce DeepGProp, a framework for genetic optimization of multilayer perceptrons that efficiently explores space of neural nets with different layers and layer size.