The Way to Optimize the Right to Personal Data Portability in Chinese Practice——Based on Empirical Analysis of the Privacy Policies of 66 Mobile Apps

Although the existing legal norms and judicial practic-es can provide basic guidance for the right to personal data portabili-ty, it can be concluded that there are obstacles to the realization of this right through empirical research of the privacy policies of 66 mobile apps, such as whether they have stipulations on the right to personal data portability, whether they are able to derive copies of personal in-formation automatically, whether there are textual examples, whether ID verification is required, whether the copied documents are encrypt-ed, and whether the scope of personal information involved is consis-tent. This gap in practice, on the one hand, reflects the misunderstand-ing of the right to personal data portability, and on the other hand, is a result of the negative externalities, practical costs and technical lim-itations of the right to personal data portability. Based on rethinking the right to data portability, we can somehow solve practical problems concerning the right to personal data portability through multiple measures such as promoting the fulfillment of this right by legislation, optimizing technology-oriented operations, refining response process mechanisms, and enhancing system interoperability.

Enhancing personalized exercise recommendation with student and exercise portraits

The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.

Personalized HRTF Prediction Based on Light GBM Using Anthropometric Data

This paper proposes a personalized headrelated transfer function(HRTF)prediction method based on Light GBM using anthropometric data.Considering the overfitting problems of the current training-based prediction methods,we use Light GBM and a specific network structure to prevent over-fitting and enhance the prediction performance.By decomposing and combining the data to be predicted,we set up 90 Light GBM models to separately predict the 90instants of HRTF in log domain.At the same time,the method of 10-fold cross-validation is used to score the accuracy of the model.For models with scores below 80 points,Bayesian optimization is used to adjust model hyperparameters to obtain a better model structure.The results obtained by Light GBM are evaluated with spectral distortion(SD)which can show the fitting error between the prediction and the original data.The mean SD values of both ears on the whole test set are 2.32 d B and 2.28 d B respectively.Compared with the non-linear regression method and the latest method,SD value of Light GBM-based method relatively decreases by 83.8%and 48.5%.

Construction of personalized knowledg environment for professional libraries:From the practice of Chinese National Agricultural Library

Purpose:Centralized on the construction of a personalized knowledge environment that can better meet the need of Chinese agricultural researchers,this paper presents the practice conducted by the National Agricultural Library(NAL)of Chinese Academy of Agricultural Sciences(CAAS)in the digital research environment.Based on the analysis of information needs of Chinese agricultural researchers,CAAS constructed digital research environments for its researchers at different levels,by means of providing more professional knowledge services for targeted researcher users.Design/methodology/approach:On the basis of the construction of public service platform—National Agriculture Information System of the Library of CAAS(i.e.NAL),local resources were integrated with public open resources by using key technologies,such as Web 2.0,knowledge navigation,linked data and intelligent retrieval,to construct an institutional personalized digital repository for CAAS researchers.Findings:By using the construction tool CAASPKE,knowledge environment platforms have been constructed for 10 CAAS institutes,taking roughly 33%of CAAS institutes.In addition,16 discipline information environment platforms have been set up for CAAS research teams,and 5 professional digital libraries for provincial agricultural academies,which spread in Beijing,Shanxi,Sichuan,and the Xinjiang and Tibet Autonomous Regions.Research limitations:User’s local collections ought to be integrated with IR resources of the CAAS platform constantly.Due to the lack of overall knowledge resources,functions of the platform have not been fully explored,so the effect need be evaluated with the time being.Practical implications:The construction of research knowledge environment in CAAS has not only contributed to the development of its personalized knowledge service system,but also made the functions of libraries transformed into the information service system.In this case,CAAS libraries are now playing more and more important roles in the innovation process of Chinese agricultural researchers.Originality/value:The innovative practice is the first endeavor that ever implemented in the agricultural information service area across China’s mainland.The construction tool developed for the knowledge environment of personalization could customize resources flexibly according to the need of different CAAS users,and it can organize the knowledge resources of CAAS institutes effectively.

Ontology-based framework for personalized recommendation in digital libraries

To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.

“Personal Digital Library”与“MyLibrary”辨析

学术界一些研究者认为"Personal Digital Library"与"MyLibrary"均可指个人数字图书馆。然而,两者是不同概念、特征及功能的事物。论文对两者的概念、研究现状及趋势作了详细的论述,旨在揭示这两种事物,为后续研究者提供借鉴。

Web-based GIS System for Real-time Field Data Collection Using Personal Mobile Phone

Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.

A Method to Improve the Accuracy of Personal Information Detection

It is necessary to confirm the personal data factors and the rules of verification before conducting personal data detection. So that the detection method can be written in the subsequent implementation of the automatic detection tool. This paper will conduct experiments on common personal data factor rules, including domestic personal identity numbers and credit card numbers with checksums. We use ChatGPT to test the accuracy of identifying personal information like ID card identification numbers or credit card numbers. And then use personal data correlation to reduce the time for personal data identification. Although the number of personal information factors found has decreased, it has had a better effect on the actual manual personal data identification. The result shows that it saves about 45% of the calculation time, and the execution efficiency of the accuracy is also improved with the original method by about 22%, which is about 2.2 times higher than the general method. Therefore, the method proposed in this paper can accurately and effectively find out the leftover personal information in the enterprise. .

Big Five Personality Factors and Library Anxiety

Library anxiety is an unpleasant feeling that is experienced in a library location;it has behavioral, psychological, emotional and cognitive effect, which can be harmful for students’ academic career. The purpose of current study was to investigate the relationship between Library anxiety and the Big Five personality factors (neuroticism, extraversion, openness-to-experience, agreeableness, and conscientiousness) using a multivariate approach among students in Ardabil university. The participants were students of Ardabil University of Medical Sciences of which a sample of 580 students was randomly selected. And the assessment methods were revised. The short form of NEO Inventory [1] and the library anxiety questionnaire [2] were used to gather the data. The results showed that Neuroticism increased library anxiety in students, and with increasing the level of education, library anxiety is reduced, whereas by increasing the semester the library anxiety of students increases.

Gamified Learning Systems’Personalized Feedback Report Dashboards via Custom Machine Learning Algorithms and Recommendation Systems

Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game design elements,accompanying pertinent pedagogical objectives of interest.This paper focuses on a cross-platform,innovative,gamified,educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments:howlearn.By applying gamification techniques,in 3D virtual environments,within which,learners fulfil STEAM(Science,Technology,Engineering,Arts and Mathematics)-related Experiments(Simulations,Virtual Labs,Interactive Storytelling Scenarios,Decision Making Case Studies),howlearn covers learners’subject material,while,simultaneously,functioning,as an Authoring Gamification Tool and as a Game Metrics Repository;users’metrics are being,dynamically,analyzed,through Machine Learning Algorithms.Consequently,the System learns from the data and learners receive Personalized Feedback Report Dashboards of their overall performance,weaknesses,interests and general class competency.A Custom Recommendation System(Collaborative Filtering,Content-Based Filtering)then supplies suggestions,representing the best matches between Experiments and learners,while also focusing on the reinforcement of the learning weaknesses of the latter.Ultimately,by optimizing the Accuracy,Performance and Predictive capability of the Personalized Feedback Report,we provide learners with scientifically valid performance assessments and educational recommendations,thence intensifying sustainable,learner-centered education.

Translation in Data Mining to Advance Personalized Medicine for Health Equity

Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.

Research on the Innovation of Personalized Education in Colleges and Universities under the Context of Big Data

The advent of the big data era has opened up unprecedented quantifiable dimensions for all walks of life,while also presented many opportunities and challenges.The widespread application of big data in the field of education will inevitably promote the innovation and transformation of the higher education in colleges and universities which introduce new opportunities and challenges to personalized education in colleges and universities.How to promote the indepth integration of big data and higher education to meet the needs of students’personalized development is one of the hot topics in the theoretical field today.In view of this,this article starts with explaining the connotation of personalized education in colleges and universities,analyzes the opportunities and challenges faced by personalized education in colleges and universities under the context of big data,and proposes the ideas of innovating personalized education in colleges and universities under the context of big data.

Opportunities and Challenges of College Mental Health Education from the Perspective of Big Data

This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issues are elucidated.Then,from the perspective of big data,the potential opportunities of big data in college mental health education are analyzed,including data-driven personalized education,real-time monitoring and warning systems,and interdisciplinary research and collaboration.At the same time,the challenges faced by college mental health education under the perspective of big data are also pointed out,such as data privacy and security issues,insufficient data analysis and interpretation capabilities,and inadequate technical facilities and talent support.Lastly,the research content of this paper is summarized,and directions and suggestions for future research are proposed.

Personal Data Use and Morality in the E-Business Environment

This paper deals with personal data use by firms in the e-business environment from the viewpoint of business administration and information ethics. Whereas the tremendous development of information and communication technology (ICT) has made it easier for firms to acquire, store, share, and utilise personal data on their customers, firms that use personal data are exposed to risks related to privacy issues. Since individuals fear the invasion of their privacy, the failure of a firm to appear or remain trustworthy would make it difficult for it to maintain accurate, up-to-date databases and to construct desirable business processes, which would affect the bottom line. Therefore, modern firms should do what they can to ensure that their customers trust them. For them, one promising way to remain trustworthy is to behave as a moral agent. Although it is difficult for any firm to meet the conditions necessary to be a moral agent, competence in behaving as a moral agent is a hard-to-imitate capability af firms for which personal data use is vital for enjoying the benefits of business relationships in the e-business environment.

On the Right to the Protection of Personal Data as a Constitutional Right

The right to the protection of personal data is an important human right in the era of big data and a constitutional right based on the national protection obligation and the theory of human dignity,making it of special significance for the realization of citizenship in a digital society.It can be seen from an examination of the constitutional texts of various countries in the world that the right to the protection of personal data as a constitutional right has rich normative connotations,and the key legal link to realize this right lies in the national legislature actively fulfilling its obligation to shape and specify the protection of personal data in accordance with the entrustment of the constitutional norms.Given the constitutional principles of fundamental rights protection,i.e.,realizing the constitutional status of the right to the protection of personal data as a basic right by means of institutional guarantees,the legislature should first adhere to the constitutionality principle of data protection legislation.Second,a multi-level data protection legal system centered on the right to the protection of personal data should be established.Finally,the institutional guarantee mechanism for the protection of personal data should be continuously improved through constitutional interpretation.

PECS: Towards Personalized Edge Caching for Future Service-Centric Networks

Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience(QoE). Existing content distribution networks(CDN) and mobile content distribution networks(mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System(PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond.

Development of a 1200 fine group nuclear data library for advanced nuclear systems

Accurate and reliable nuclear data libraries are essential for calculation and design of advanced nuclea systems. A 1200 fine group nuclear data library Hybrid Evaluated Nuclear Data Library/Fine Group(HENDL/FG with neutrons of up to 150 Me V has been developed to improve the accuracy of neutronics calculations and anal ysis. Corrections of Doppler, resonance self-shielding, and thermal upscatter effects were done for HENDL/FG Shielding and critical safety benchmarks were performed to test the accuracy and reliability of the library. The dis crepancy between calculated and measured nuclea parameters fell into a reasonable range.

Securing Patient-Centric Personal Health Records Sharing System in Cloud Computing

Personal health record （PHR） enables patients to manage their own electronic medical records （EMR） in a centralized way, and it is oRen outsourced to be stored in a third-party server. In this paper we propose a novel secure and scalable system for sharing PHRs. We focus on the multiple data owner scenario, and divide the users in the system into multiple security domains that greatly reduce the key management complexity for owners and users. A high degree of patient privacy is guaranteed by exploiting hierarchical and multi- authority attribute-sets based encryption （HM- ASBE）. Our system not only supports compound attributes due to flexible attribute sets combinations, but also achieves fine-grained access control. Our scheme supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios.

Establishment of Person and Enterprise Process Model in Product Data Management System

Before the implementation of product data management (PDM) system, person model and enterprise process model (EPM) must be firstly established. For the convenience of project management, all the related users must be allocated to the “Person User Role Group” net. Based on the person model and the direction of information flow, the EPM is established subsequently. The EPM consists of several release levels, in which the access controls are defined. The EPM procedure shows the blueprint of the workflow process structure. The establishment of person model and EPM in an enterprise has been instanced at the end of this paper.

Data organization and management of mine typical object spectral libraries

With the development of mining industry,people have obtained profits from it,but they are facing environmental damages.In order to monitor these environmental changes,a spectral library is set up for the spectrum data organization and management of mine typical objects.Most of the spectrum data come from the long-term field measuring in mining area and other spectral libraries.For the data quality control and error detection in the measuring data,an inner precision calculation method is presented and a series of interactive graphical controls are developed for the spectrum visualization and analysis.Through extracting and saving spectrum characters for the mine typical objects,realizs spectrum matching and classification for new measured spectrum samples are realized by using Euclidean distance,Aitchison distance,Pearson correlation coefficient and vector angular cosine methods.Based on the matching result,this work is able to gather dynamically physicochemical environment parameters from the library and gives an early warning for the mine environmental changes.