Unravelling the Complex Networks of Social Physics:Exploring Human Behavior,Big Data,and Distributed Systems

Networks form an integral part of our daily lives, comprising structures known as nodes connected via ties. These networks exhibit a complex interplay of random and structured features. We encounter networks regularly in our daily activities, whether it is logging into websites, commuting by train, going to work, or attending school. Networks are prevalent in various academic disciplines and practical applications, spanning biology, physics, engineering, social science, and numerous other fields. The early 21st century has witnessed a rapid expansion of techniques facilitating human interaction, significantly impacting behavior and social bonding in both urban and rural settings. The availability and accessibility of extensive population datasets have spurred ongoing research into understanding and shaping human inclinations and sociological phenomena in unconventional ways. This review delves into the nuanced aspects of social physics, exploring the rationale behind choosing this research topic, various conceptual frameworks, categorizations, and distinctions between machine learning and deep learning. The article briefly outlines the advantages of leveraging big data in social physics, highlighting its transformative potential. Lastly, the authors provide insights into several applications of social physics in subsequent sections, offering a glimpse into its real-world implications.

(r,QI)-Transform：Reversible Data Anonymity Based on Numeric Type of Data in Outsourced Database

An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capacity.However,the outsource database still has some challenges.If the service provider does not have sufficient confidence,there is the possibility of data leakage.The data may has user＇s privacy,so data leakage may cause data privacy leak.Based on this factor,to protect the privacy of data in the outsource database becomes very important.In the past,scholars have proposed k-anonymity to protect data privacy in the database.It lets data become anonymous to avoid data privacy leak.But k-anonymity has some problems,it is irreversible,and easier to be attacked by homogeneity attack and background knowledge attack.Later on,scholars have proposed some studies to solve homogeneity attack and background knowledge attack.But their studies still cannot recover back to the original data.In this paper,we propose a data anonymity method.It can be reversible and also prevent those two attacks.Our study is based on the proposed r-transform.It can be used on the numeric type of attributes in the outsource database.In the experiment,we discussed the time required to anonymize and recover data.Furthermore,we investigated the defense against homogeneous attack and background knowledge attack.At the end,we summarized the proposed method and future researches.

A Bibliometric Analysis of Public Diplomacy Research in China

Public diplomacy is a new branch of foreign policy that has developed as a result of globalization. Emerging international conditions havereshaped public diplomacy--something which has been around for a while--and endowed it with new connotations and goals： that is to inform the international community about the history and current status of a country; to seek widespread international understanding and support for a country＇ s foreign affairs; and to help establish a positive intemational image and credibility which will in turn win the country intemational recognition and cooperation, thus serving its national interests.

Analysis of Transforming dq Impedances of Different Converters to A Common Reference Frame in Complex Converter Networks

DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.

A New Quality Improving Scheme for VQ Decompressed Images Based on DWT

The better compression rate can be achieved by the traditional vector quantization （VQ） method, and the quality of the recovered image can also be accepted. But the decompressed image quality can not be promoted efficiently, so how to balance the image compression rate and image recovering quality is an important issue, in this paper, an image is transformed by discrete wavelet transform （DWT） to generate its DWT transformed image which can be compressed by the VQ method further. Besides, we compute the values between the DWT transformed image and decompressed DWT transformed image as the difference matrix which is the adjustable basis of the decompressed image quality. By controlling the deviation of the difference matrix, there can be nearly Iossless compression for the VQ method. Experimental results show that when the number of compressed bits by our method is equal to the number of those bits compressed by the VQ method, the quality of our recovered image is better. Moreover, the proposed method has more compression capability comparing with the VQ scheme.

Quality Criteria for B2C E-Commerce Websites in Brazil

The objective of this study is to compare the ranking of the quality of e-commerce B2C portals in Brazil according to explicit criteria described in the article with the ranking according to the INFO 100 ranking. Theoretical research is first described, followed by the construction of eight self-established criteria of B2C portal quality measures and identification and compilation of requirements used by the I-Best, Opinia, E-bit and other institutes. After that, a portal quality evaluation is made by the web site content analysis;finally, a comparison of the rankings is presented. The obtained results showed that in spite of the lack of knowledge of the criteria adopted by INFO100 [1], there could be agreement with the criteria adopted in this study, because there was agreement with the results for the Ponto Frio portal. Therefore, it was concluded that the eight criteria used in this study can be useful in a quality analysis of B2C electronic portals.

Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm

In Computer-Aided Detection(CAD)brain disease classification is a vital issue.Alzheimer’s Disease(AD)and brain tumors are the primary reasons of death.The studies of these diseases are carried out by Magnetic Resonance Imaging(MRI),Positron Emission Tomography(PET),and Computed Tomography(CT)scans which require expertise to understand the modality.The disease is the most prevalent in the elderly and can be fatal in its later stages.The result can be determined by calculating the mini-mental state exam score,following which the MRI scan of the brain is successful.Apart from that,various classification algorithms,such as machine learning and deep learning,are useful for diagnosing MRI scans.However,they do have some limitations in terms of accuracy.This paper proposes some insightful pre-processing methods that significantly improve the classification performance of these MRI images.Additionally,it reduced the time it took to train the model of various pre-existing learning algorithms.A dataset was obtained from Alzheimer’s Disease Neurological Initiative(ADNI)and converted from a 4D format to a 2D format.Selective clipping,grayscale image conversion,and histogram equalization techniques were used to pre-process the images.After pre-processing,we proposed three learning algorithms for AD classification,that is random forest,XGBoost,and Convolution Neural Networks(CNN).Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57%and sensitivity is 97.60%.

Resilience

Based on the integration of psychology,behavioral science,and other cutting-edge science,the authors of this book propose the"Resilience Flyer"model,which aims to decode anxiety and emotions from three dimensions:awareness,meaning,and connection,to discover and intensify the ability tolove,to strengthen self-awareness in relationships,and realize the transmission from individual resilience to organizational resilience.