Digital Development Rights in Developing Countries:Where the Governance Rules for Cross-Border Data Flows

The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.

INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION

Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.

Functionality Semantics of Predicate Data Flow Diagram

SOZL (structured methodology + object-oriented methodology + Z language) is a language that attempts to integrate structured method, object-oriented method and formal method. The core of this language is predicate data flow diagram (PDFD). In order to eliminate the ambiguity of predicate data flow diagrams and their associated textual specifications, a formalization of the syntax and semantics of predicate data flow diagrams is necessary. In this paper we use Z notation to define an abstract syntax and the related structural constraints for the PDFD notation, and provide it with an axiomatic semantics based on the concept of data availability and functionality of predicate operation. Finally, an example is given to establish functionality consistent decomposition on hierarchical PDFD (HPDFD).

A New Automated Method and Sample Data Flow for Analysis of Volatile Nitrosamines in Human Urine

Volatile nitrosamines (VNAs) are a group of compounds classified as probable (group 2A) and possible (group 2B) carcinogens in humans. Along with certain foods and contaminated drinking water, VNAs are detected at high levels in tobacco products and in both mainstream and side-stream smoke. Our laboratory monitors six urinary VNAs—N-nitrosodimethylamine (NDMA), N-nitrosomethylethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosopiperidine (NPIP), N-nitrosopyrrolidine (NPYR), and N-nitrosomorpholine (NMOR)—using isotope dilution GC-MS/ MS (QQQ) for large population studies such as the National Health and Nutrition Examination Survey (NHANES). In this paper, we report for the first time a new automated sample preparation method to more efficiently quantitate these VNAs. Automation is done using Hamilton STARTM and Caliper StaccatoTM workstations. This new automated method reduces sample preparation time from 4 hours to 2.5 hours while maintaining precision (inter-run CV < 10%) and accuracy (85% - 111%). More importantly this method increases sample throughput while maintaining a low limit of detection (<10 pg/mL) for all analytes. A streamlined sample data flow was created in parallel to the automated method, in which samples can be tracked from receiving to final LIMs output with minimal human intervention, further minimizing human error in the sample preparation process. This new automated method and the sample data flow are currently applied in bio-monitoring of VNAs in the US non-institutionalized population NHANES 2013-2014 cycle.

Exploring the Approaches to Data Flow Computing

Architectures based on the data flow computing model provide an alternative to the conventional Von-Neumann architecture that are widelyused for general purpose computing.Processors based on the data flow architecture employ fine-grain data-driven parallelism.These architectures have thepotential to exploit the inherent parallelism in compute intensive applicationslike signal processing,image and video processing and so on and can thusachieve faster throughputs and higher power efficiency.In this paper,severaldata flow computing architectures are explored,and their main architecturalfeatures are studied.Furthermore,a classification of the processors is presented based on whether they employ either the data flow execution modelexclusively or in combination with the control flow model and are accordinglygrouped as exclusive data flow or hybrid architectures.The hybrid categoryis further subdivided as conjoint or accelerator-style architectures dependingon how they deploy and separate the data flow and control flow executionmodel within their execution blocks.Lastly,a brief comparison and discussionof their advantages and drawbacks is also considered.From this study weconclude that although the data flow architectures are seen to have maturedsignificantly,issues like data-structure handling and lack of efficient placementand scheduling algorithms have prevented these from becoming commerciallyviable.

Data Flow Fuzzy Control of Communication Network with Finite Terminal

We consider the problem of data flow fuzzy control of discrete queuing systems with three different service-rate servers. The objective is to dynamically assign customers to idle severs based on the state of the system so as to minimize the mean sojourn time of customers. Simulation shows the validity of the fuzzy controller.

An Algorithm of Programming Data Flow Analysis Based on Data Flow Expression

This paper states the basic principle of program data flow analysis in a formal way and gives the concept of data flow expression. On the basis of this concept, an algorithm of finding data flow exceptions is rendered. This algorithm has great generality, with which it is easy to develop a tool for program test. So it is practical in application.

Determination of Curie Point Depth and Heat Flow Using Airborne Magnetic Data over the Kom-Ombo and Nuqra Basins, Southern Eastern Desert, Egypt

The Kom-Ombo and Nuqra basins in southern Egypt have recently been discovered as potential hydrocarbon basins. The lack of information about the geothermal gradient and heat flow in the study area gives importance to studying the heat flow and the geothermal gradient. Several studies were carried out to investigate the geothermal analyses of the northwestern desert, as well as the west and east of the Nile River, using density, compressive wave velocity, and bottom hole temperature (BHT) measured from deep oil wells. This research relies on spectral analysis of airborne magnetic survey data in the Kom-Ombo and Nuqra basins in order to estimate the geothermal gradient based on calculating the depth to the bottom of the magnetic source that caused the occurrence of these magnetic deviations. This depth is equal to the CPD, at which the material loses its magnetic polarisation. This method is fast and gives satisfactory results. Usually, it can be applied as a reconnaissance technique for geothermal exploration targets due to the abundance of magnetic data. The depth of the top (Zt) and centroid (Z0) of the magnetic source bodies was calculated for the 32 windows representing the study area using spectral analysis of airborne magnetic data. The curie-isotherm depth, geothermal gradient, and heat flow maps were constructed for the study area. The results showed that the CPD in the study area ranges from 13 km to 20 km. The heat flow map values range from 69 to 109 mW/m2, with an average of about 80 mW/m2. The calculated heat flow values in the assigned areas (A, B, C, and D) of the study area are considered to have high heat flow values, reaching 109 mW/m2. On the other hand, the heat flow values in the other parts range from 70 to 85 mW/m2. Since heat flow plays an essential role in the maturation of organic matter, it is recommended that hydrocarbon accumulations be located in places with high heat flow values, while deep drilling of hydrocarbon wells is recommended in places with low to moderate heat flow values.

A Testing Method for Web Services Composition Based on Data-Flow

This paper proposes a method of data-flow testing for Web services composition. Firstly, to facilitate data flow analysis and constraints collecting, the existing model representation of business process execution language （BPEL） is modified in company with the analysis of data dependency and an exact representation of dead path elimination （DPE） is proposed, which over-comes the difficulties brought to dataflow analysis. Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language （WSDL） documents and the def-use annotated control flow graph is created. Based on this model, data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph, and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated, then testers can design the test cases according to the collected constraints for each path selected.

Towards a Global Regulatory Framework for Cross-Border Data Flows——Fundamental Concerns and the China’s Approach

Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digital trade negotiations could not accommodate these challenges,China has initiated the concept of secure cross-border data flow and has launched a dual-track multi-level regulatory system,including control system for overseas transfer of important data,system of crossborder provision of personal information,and system of cross-border data request for justice and enforcement.To explore a global regulatory framework for cross-border data flows,legitimate and controllable cross-border data flows should be promoted,supervision should be categorized based on risk concerned,and the rule of law should be coordinated at home and abroad to promote system compatibility.To this end,the key is to build a compatible regulatory framework,which includes clarifying the scope of important data to define the“Negative List”for preventing national security risks,improving the cross-border accountability for protecting personal information rights and interests to ease pre-supervision pressure,and focusing on data access rights instead of data localization for upholding the jurisdiction of justice and enforcement.

The Governance of Cross-Border Data Flows in Trade Agreements:Is the CPTPP Framework an Ideal Way Out?

The regulations of cross-border data flows is a growing challenge for the international community.International trade agreements,however,appear to be pioneering legal methods to cope,as they have grappled with this issue since the 1990s.The World Trade Organization(WTO)rules system offers a partial solution under the General Agreement on Trade in Services(GATS),which covers aspects related to cross-border data flows.The Comprehensive and Progressive Agreement for Trans-Pacific Partnership(CPTPP)and the United States-Mexico-Canada Agreement(USMCA)have also been perceived to provide forward-looking resolutions.In this context,this article analyzes why a resolution to this issue may be illusory.While they regulate cross-border data flows in various ways,the structure and wording of exception articles of both the CPTPP and USMCA have the potential to pose significant challenges to the international legal system.The new system,attempting to weigh societal values and economic development,is imbalanced,often valuing free trade more than individual online privacy and cybersecurity.Furthermore,the inclusion of poison-pill clauses is,by nature,antithetical to cooperation.Thus,for the international community generally,and China in particular,cross-border data flows would best be regulated under the WTO-centered multilateral trade law system.

A New Synthetical Knowledge Representation Model and Its Application in Data Flow Diagram

A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of syntax and semantic rules suitable for a semantic network. Based on the model,the paper introduces a formal method defining data flow diagrams (DFD) and also simply explains how to use the method.

A Survey of Malware Classification Methods Based on Data Flow Graph

Malware is emerging day by day.To evade detection,many malware obfuscation techniques have emerged.Dynamicmalware detectionmethods based on data flow graphs have attracted much attention since they can deal with the obfuscation problem to a certain extent.Many malware classification methods based on data flow graphs have been proposed.Some of them are based on userdefined features or graph similarity of data flow graphs.Graph neural networks have also recently been used to implement malware classification recently.This paper provides an overview of current data flow graph-based malware classification methods.Their respective advantages and disadvantages are summarized as well.In addition,the future trend of the data flow graph-based malware classification method is analyzed,which is of great significance for promoting the development of malware detection technology.

Detecting Data-flow Errors Based on Petri Nets With Data Operations

In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.

Study on the Deconvolution Method and Processing Flow of Airgun Source Data

With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source signals. To some extent,deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking,in order to remove the airgun source wavelet signal and obtain the Green's functions between the airgun source and stations,we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions,etc. We use the Binchuan( in Yunnan Province,China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency;time domain iterative deconvolution is better in terms of the signal-to-noise ratio( SNR),and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally,we propose a general processing flow for the airgun source data to extract the Green 's functions between the airgun source and stations.

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.

PERFORMANCE ANALYSIS OF STATIC DATA FLOW COMPUTERS

First, a model of static data flow computer and a model of data flow graph are pro-posed; then a model of system is presented to calculate practical parallelism degree withoverhead of instruction execution on data flow computers as its parameter. From the compu-tation, the maximum practical parallelism degree of a program running on a static dataflow computer is determined with MP/OH (MP is the mean parallelism degree of a program,OH is the overhead of instruction execution on the computer). Therefore the overhead hasgreat influence on the performance of a data flow computer.

IS: Interest Set to Enhance Flow Transmission in Named-Data Networking

Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop traffic control. And those elements ensure data holders to solely return the requested data within the lifetime of the request, instead of pushing data whenever needed and whatever it is. Despite the dispute on the advantages and their prices, this pattern requires data consumers to keep sending requests at the right moments for continuous data transmission, resulting in significant forwarding cost and sophisticated application design. In this paper, we propose Interest Set(IS) mechanism, which compresses a set of similar Interests into one request, and maintains a relative long-term data returning path with soft state and continuous feedback from upstream. In this way, IS relaxes the above requirement, and scales NDN data forwarding by reducing forwarded requests and soft states that are needed to retrieve a given set of data.

基于HCPN的复杂BPMN协作模型数据流建模与验证

为了保证复杂BPMN协作模型的正确性,不仅要涵盖多实例和子进程等复杂元素,还要在检测控制流错误的同时检测数据流错误。但业务流程建模标注(BPMN 2.0)缺乏形式化语义的描述,这对模型正确性的验证造成了阻碍。因此,给出了一种具有弧权重的层次化着色Petri网(HCPN)的定义,它既可以对数据流进行形式化表示,又可以对多实例和子进程结构进行建模。进一步提出了从BPMN协作模型到HCPN模型的形式化映射方法。然后基于HCPN模型的弧权重给出了缺失、丢失和冗余3种数据流错误的定义,并提出了对应的检测算法。最后,设计了一个自动化建模与验证的框架,通过一个案例研究说明了该方法的有效性。

面向图书馆数字化转型的数据架构内涵、方法与策略

[目的/意义]数据架构作为一个将数字技术融入业务、实现组织在数字空间发展的方法论框架,是解决图书馆等传统组织在数字空间数字化解构与重组等实际问题,促进数字化转型与智慧化发展的关键。[方法/过程]结合当前图书馆数字化进程中的理论研究与实践现状,首先对数据架构相关概念与定义、功能与目标进行分析,得到图书馆数据架构的内涵;在此基础上,提出了图书馆数据架构的建构方法;最后,系统地探讨了图书馆实施数据架构的路径与策略。[结果/结论]图书馆数据架构主要基于数据思维,通过业务解构和业务重组,实现业务逻辑由物理空间向数字空间的翻译与转化;可通过对关键业务实体、具体场景的数据建模,以及数据流动与整合规划等图书馆数据架构策略,为实现图书馆数字化转型服务。