ISIS Summit Vienna 2015—The Information Society at the Crossroads : Conference Track: China and the global information society Basic Law of Information：the Fundamental Theory of Generalized Bilingual Processing Xiaohui ZOU * and Shunpeng ZOU * http://in.linkedin.com/pub/xiaohui-zou-邹晓辉/37/46/158 23 February 2015 Abstract The purpose of this paper is generally to introduce the basic law of information. The method is: the generalized bilingual information processing, especially by comparing the two kinds of formal strategy, on the one hand, inherited 'language understanding, knowledge representation, pattern recognition' software engineering approach, on the other hand, to create 'generalized bilingual, knowledge ontology, bilingual programming' systems engineering strategies. The result: standardization and personalized or diversified combination of three aspects, to achieve the best effect of human-computer interaction. Its significance lies in: the Turing test based on English - with Turing computability thesis - view of strong artificial intelligence, the Turing test based on Chinese - with Searle “Chinese room” argument--view of weak artificial intellignence, the two extremes of thesis, both can be seen as the Turing test based on generalized bilingual - as two exceptions within Xiaohui “double chessboard” thesis, thereby, underline the view of basic law of information, and its practical application value. Keywords Information Law，Artificial Intelligence，Human-computer Interaction，Generalized Bilingual Processing 信息基本定律--广义双语处理的基础理论 1．引言 本文旨在通俗地介绍信息基本定律--广义双语处理的基础理论。 其中双语涉及三类：狭义双语，如中英文；另类双语，如术语和俗语；广义双语，如数学语言（算术数字）和自然语言（汉字）。 双语信息处理方法由人际、人机、机际、机人之间一系列双语转换步骤所组成，遵循三大信息定律。其核心困难是翻译和机译中如何化解歧义，这也是本文的焦点。 2. 方法 通过广义双语信息处理，尤其是通过比较两大类形式化方略，一方面，继承“语言理解、知识表达、模式识别”软件工程方略，另一方面，创造“广义双语、知识本体、双语编程”系统工程方略。 本文在两大类形式化方略的研究基础之上，暂时抛开以往形式化路径等技术问题而只讨论我们对比研究的结果，即：“广义双语、知识本体、双语编程”的系统工程方略。 下面重点介绍可操作的三个基本步骤及其配套的三个模型及其理论依据，涉及宏观微观贯通的两类实例。 步骤1及模型1：笔者提炼的汉英翻译蝴蝶模型是在韦弗和沃古瓦的研究成果基础之上发展出来的。前人曾设想基于统计和规则的机译存在中间语，笔者发现事实上没有,最多可以说一系列双语对中的一个可作为“中介语”，关键是双语对的建构。 步骤2及模型2：笔者提炼的知识和常识“双识”本体模型，从总体上优化了人类认知发展与知识积累进程的几个基本范畴，包括最基本的定性分析的“语义三棱”和细化的定性分析“三跨划分”。前者描绘出整个人类知识体系。后者则在跨学科、跨领域、跨行业的术语即知识细分体系之间内架起一道桥梁。 步骤3及模型3：笔者建构的三类双语信息处理系统即“协同”模型，其中广义双语微观操作模型及其实用的关系数据库，可结合索绪尔曾把语言系统比喻的“棋”以及维特根斯坦曾比喻的“语言游戏”，同时再结合模型1和2，即可形成三类双语的超级“棋”和大跨度的“语言游戏”，各式各样的歧义均可在此进程中，遵循三大定律而逐步化解。 3. 结果 其结果是：标准化与个性化、多元化、多样化三方面的结合，达到最佳的人机交互效果。 信息基本定律A“序位关系，唯一守恒”即Id=n n； 信息基本定律B“同义并列，对应转换”即Id=Ik+Iu； 信息基本定律C“同意并列，对应转换”即Ik+Iu=∑m m。 三类双语转换模型1-3与三大信息基本定律ABC之间的关系：A3B；1C2。 广义双语信息处理方法的有效性在于：不仅在人际、人机、机际、机人之间，而且在三类双语之间，都做到了合理分工、优势互补；高度协作、优化互动（16字方针）。 4. 结论 其意义是图灵“可计算性”论题和塞尔“中文房间”论题可被视为晓辉“双字棋盘”论题的两个特例，从而突显信息基本定律及其实际应用价值。 其意义可进一步描述如下： 从理论上开阔思路： 可兼容形式信息论的收敛性与语义信息论的开放性。前者的特点是形式化和可计算；后者的特点是多样化和复杂性。 在实践中发挥作用： 采用广义双语信息处理方法可超越强弱两派的人工智能观点，在三类双语协同处理的系统中实现精准的机器翻译。这是普通的自然人和单纯的计算机都分别做不到的。 可以说，发现了三大信息基本定律就明确了“三类双语”协同互译的依据；实现了广义双语信息处理，也就证明了三类双语协同互译机制存在。它们之间是互为因果的关系。
汉语是联合国官方正式使用的 6 种同等有效语言之一。请不要歧视汉语！ Chinese is one of the six equally effective official languages of the United Nations. Not to discriminate against Chinese, please! 安培力定律（Ampère's force law） 与 洛伦兹力（Lorentz force） 的 高精度现代检验 在宏观低速情况下，用高稳定性高精度的现代实验， 重新进行安培力定律（Ampère's force law）与洛伦兹力（Lorentz force）的实验检验。 万一当初的实验精度与稳定性不够，里面有某些偏差怎么办？ 起因： 统一场！ 引力相互作用和电磁相互作用， 至今未见公认的良好统一。 有没有下述可能： 即 安培力定律（Ampère's force law） 洛伦兹力（Lorentz force） 等 经典实验的精度不够 ， 我们现在看到的方程形式不是很准确？ 物理学， 归根到底是实验科学。 理论， 归根到底来自实验。 参考文献： Williams E R, Faller J E, Hill H A. New experimental test of Coulomb's law: a laboratory upper limit on the photon rest mass . Physics Review Letters, 1971, 26(12): 721-724. http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.26.721 基础课的精华性（引力磁），《高教研究与探索》，1997，2：pp34-36. Lorentz force - From Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Lorentz_force Ampère's force law - From Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Amp%C3%A8re%27s_force_law Newton's law of universal gravitationw - From Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Newton%27s_law_of_universal_gravitation 安培力定律 （Ampère's force law） 与洛伦兹力 （Lorentz force） 的高精度现代检验 http://bbs.sciencenet.cn/thread-2276084-1-1.html
Didier Sornette and Guy Ouillon, Dragon-kings:mechanisms, statistical methods and empirical evidence, The European Physical Journal Special Topics, 205,1-26, 2012. According to this paradigm ( 指的是 power law 统计，也特指 Per Bak 等人的 SOC 理论对 power-law 的阐释 ), small, large and extreme events belong to the same population, the same distribution, and reflect the same underlying mechanism(s). Following that reasoning, a majority of the scientific community considers those events as unpredictable, in the sense that the final size of a future cannot be forecasted in advance （ 我们搞大气的大概不是 scientific community 或者说不是 major scientific community. 我们天天花那多钱搞高影响天气气候的预报，在主流科学团体看来简直是浪费 ） . This view is particularly pessimistic, and even alarming if true, as it casts a strong doubton possibilities of precise hazard prediction, with all the societal consequences. In our mind, it is even dangerous as it promotes an attitude of irresponsibility: in a world where catastrophes are pure surprises, no one can be responsible （ 对 SOC 的檄文，也算是帮我们搞大气的说了句话，看来大气加入 major scientific community 有门 ） . In contrast,the concept of dragon-kings, if their occurrences can be diagnosed ex-ante,brings back responsibility and accountability （ 说了这么一堆，杀手锏登场亮相， that is DRAGON-KING ） . Didier Sornette, Dragon-Kings, Black Swans and the Prediction of Crises, International Journal of Terraspace Sciences and Engineering, 2, 1-18, 2009. There is a growing recognition that progress in most of these disciplines, …, will need such a systemic complex system and multidisciplinary approach. This view tends to replace the previous “analytical” approach, consisting of decomposing asystem in components, such that the detailed understanding of each component was believed to bring understanding in the functioning of the whole. （ 不忘时时刻刻给还原论泼泼冷水 ）
摩尔定律让位于贝索斯定律 Moore’s law gives way to Bezos’s law 摘要： 摩尔定律揭示了当价格不变时，集成电路上可容纳的晶体管数，约每隔18个月会增加一倍，性能也将提升一倍。而本文作者提出了贝索斯定律，以亚马逊CEO贝索斯名字命名，以此来预测单位计算能力的价格和时间的关系。 【编者按】相对于构建昂贵的数据中心和私有云，将您的应用迁移到云上无疑能给你省下一大笔硬件费用和人力费用，而云计算的未来肯定是朝着以更低的成本获取更多的计算能力方向发展，现在以AWS和Google为主的云商们，纷纷调整战略来促使云的快速增长，带来的结果正如本文所述的那样：在云的发展过程中，单位计算能力的价格大约每隔3年会降低50%。而作者将其命名为贝索斯定律。下面看Gigaom的Greg O'Connor给我们带来的精彩解读。 以下为原文： 云计算未来发展的趋势必然是以更低的成本获取更多的计算能力。云供应商Google、Amazon Web Services（AWS）还有微软正在进行业务大清洗，为了定价上占有优势，它们将旧的产品和业务淘汰掉，并且引入新的产品和业务。这些最新的战略调整凸显出全新的、明确的商业模式，而这些模式将会促使云以指数增长方式发展，并且在定价上将带来重大改进——正如摩尔定律对电脑计算能力的预测。 贝索斯定律 如果你需要更深刻的理解这一点，可以参考一下摩尔定律——当价格不变时，集成电路上可容纳的晶体管数目，约每隔18个月便会增加一倍，性能也将提升一倍。鉴于此，我提出了我的版本：贝索斯定律。我以亚马逊首席执行官贝索斯的名字来给这一定律命名，并将它定义为： 在云的发展过程中，单位计算能力的价格大约每隔 3年会降低50%（over the history of cloud, a unit of computing power price is reduced by 50 percent approximately every three years.）。 下面我会用一些数据做分析，通过数学计算来说明，假如贝索斯定理正确的反映了现实，那唯一的结论是大多数企业都应该抛弃他们的数据中心，并且将他们的应用移动到公有云，以节省资金。根据摩尔定律可知，随着时间的推移，企业会节约一笔可观的硬件费用，此外还会节约一大笔固定维修费用、电力费用、冷却费用以及数据中心构建、运营和维护的人力费用。在本文最后，我会演示如何做到每1年价格减少20%、每3年费用减少一半（I’ll show how prices are reduced by about 20 percent per year, cutting your bill in half every three years.） 。 用数据说明问题 Google首先宣布了按照定价需求进行业务调整。为了证明过去云定价策略已经不适用，Google的UrsHölzle在三月份向我们说明了云定价并不遵循Moore定律：过去的5年里，硬件成本平均每年下降率约20%到30%，但是公有云价格平均每年仅下降8%。 看完AWS的报告后，我又用数学方法做了一些计算得出：在过去八年中，公有云价格削减了43次。每年下降6%到8%的说法似乎不太靠谱（8年每年降价8%，通过计算可知，平均每次下降2%的话得降价43次）。 尽管如此，应用摩尔定律来得出云变化率，保持计算单元不变，得到的定价结果要比实际值还要低。 因此，我们可以先假设贝索斯定律的结论是：随着云的发展，每Y年单位计算能力的价格会下降X%。 通过对AWS企业博客的挖掘，可以发现计算能力和时间周期（2008年5月29日）之间的关系，X表示计算能力的百分比，Y表示时间周期。2008年一直到2014年4月1日的数据集中显示了这6年类似云服务实例中，普通型云服务以16%的速率在降价，超大型云服务以20%的速率在降价。假设以时间线为基准，那云定价正如下表显示的那样： 对于AWS的公有云，当Y（时间周期）=3年时，X（计算能力半分比）=50%。这恰好证明了我的贝索斯定律猜想——在云的发展过程中，单位计算能力的价格大约每3年会降低50%。 云,数据中心,企业该如何选择 ( What’s next ) 显然，云与建立和维护数据中心不一样，相比之下，对于大多数公司来说，云是更为经济的选择。 一个企业数据中心怎样才能跟上亚马逊、IBM、Google和微软等巨头们创新的步伐？企业的技术专家们知道答案，如果你问他们，他们会反问：“我们为什么要花那么高的代价构建所谓的数据中心或者私有云？” 谈到盈利，云供应商似乎不可能像零售企业那么成功——这样说也许为时过早，就像IBM将x86的服务器业务卖给联想那样。着眼于长远的利益，未来将产生长期基于核心云平台的创新，这或许会给未来云服务暗淡的盈利前景带来改变。 不同的意见先放在一边，毕竟数据不会说谎。企业总有一天会迁移到云平台，这一点毫无疑问，关键是什么时候迁，怎样迁？ 英文原文和中文翻译： http://gigaom.com/2014/04/19/moores-law-gives-way-to-bezoss-law/ http://www.csdn.net/article/2014-04-21/2819416-Cloud-BigData
Contents 1 Kai Hwang PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing Fuzzy Trust Integration for Security Enforcement in Grid Computing 2 1 Kai Hwang PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing Runfang Zhou,and Kai Hwang IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS，2007 Abstract： Peer-to-Peer (P2P) reputation systems are essential to evaluate the trustworthiness of participating peers and to combat the selfish, dishonest, and malicious peer behaviors. The system collects locally -generated peer feedbacks and aggregates them to yield the global reputation scores. Surprisingly, most previous work ignored the distribution of peer feedbacks . We use a trust overlay network (TON) to model the trust relationships among peers. After examining the eBay transaction trace of over 10,000 users, we discover a power-law distribution in user feedbacks. Our mathematical analysis justifies that power-law distribution is applicable to any dynamically growing P2P systems, either structured or unstructured. We develop a robust and scalable P2P reputation system, PowerTrust, to leverage the power-law feedback characteristics. The PowerTrust system dynamically selects small number of power nodes that are most reputable using a distributed ranking mechanism. By using a look-ahead random walk strategy and leveraging the power nodes, PowerTrust significantly improves in global reputation accuracy and aggregation speed. PowerTrust is adaptable to dynamics in peer joining and leaving and robust to disturbance by malicious peers. Through P2P network simulation experiments, we find significant performance gains in using PowerTrust. This power-law guided reputation system design proves to achieve high query success rate in P2P file-sharing applications. The system also reduces the total job makespan and failure rate in large-scale, parameter-sweeping P2P Grid applications. Index Terms： Peer-to-Peer system, overlay network, distributed hash table, reputation system, eBay trace data set, distributed file sharing, P2P Grids, PSA benchmark, system scalability. 1 INTRODUCTION reputation management examples: eBay and its drawbacks trend: calculate the global reputation scores by aggregating peer feedbacks in adistributed manner six key issues for designing a cost-effective P2P reputation system: . High accuracy . Fast convergence speed . Low overhead . Adaptive to peer dynamics . Robust to malicious peers . Scalability the common ignorance of previous work: ignored the distribution of peer feedbacks or assumed an arbitrary random distribution the organization of this paper: 1) Section 2 reviews existing work on P2P reputation systems. 2) Section 3 introduce the new PowerTrust system concept and the use of trust overlay network. 3) Section 4 analyze the eBay trace data to reveal the powerlaw distribution of peer feedbacks. 4) Section 5 specifies the detailed design ofPowerTrust system and the reputation aggregation algorithms used. 5)Section 6 evaluate the performance attributes of the PowerTrust system 6) Section 7 report its application benchmark results 2 RELATED WORKS 3 OUR POWERTRUST SYSTEM APPROACH 3.1 The PowerTrust System Concept five functional modules of the system: 1) The regular random walk module: supports the initial reputation aggregation. 2) The look-ahead random walk (LRW) module: to update the reputation score,periodically. 3)a distributed ranking module: to identify the power nodes. 3.2 Trust Overlay Network (TON) TON: a virtual network on top of a P2P system 4 POWER-LAW DISTRIBUTION OF PEER FEEDBACKS Power-law distribution Three key parameters: 1) The feedback amount of a node 2) Feedback frequency 3) The ranking index 4.1 Collection Procedure of eBay Reputation Data 4.2 Feedback Distribution in eBay Reputation Data linear-regression 4.3 Feedback Distribution Analysis in P2P Systems two factors of the power-law feedback distribution : 1) dynamic growth of TON size 2) preferential node attachment next step: why power-law feedback distribution applies to P2P reputation systems in general? 5 POWERTRUST SYSTEM CONSTRUCTION the outline of this section : to give three construction algorithms 1) the initial construction, 2) distributed ranking, 3) updating process of the PowerTrust system. 5.1 Look-Ahead Random Walk (LRW) PowerTrust: feedback scores are generated by Bayesian learning or by an average rating the trust matrix R the eigenvector of the trust matrix R recursive process: motivated by the Markov random walk, 5.2 Distributed Ranking Mechanism PowerTrust; uses a Distributed Hash Table (DHT)to implement the distributed ranking mechanism. locality preserving hashing (LPH) 5.3 Initial Global Reputation Aggregation 5.4 Global Reputation Updating Procedure 6 SYSTEM PERFORMANCE ANALYSIS analyzed criterion: 1) reputation convergence overhead, 2) ranking discrepancy, 3) aggregation errors by malicious peers 6.1 Simulation Setup and Experiments Performed 6.2 Reputation Convergence Overhead convergence overhead : measured as the number of iterations before the global reputation convergence vs EigenTrust approach 6.3 Reputation Ranking Discrepancy 6.4 Effects of Malicious Peer Behaviors root-mean-square (RMS) 7 P2P APPLICATION BENCHMARK RESULTS the benchmark: PSA, parameter sweeping applications 7.1 Query Success Rate in Distributed File Sharing 7.2 P2P Grid Performance over the PSA Workload 8 CONCLUSIONS AND FURTHER WORK the purpose of this paper : the design experiences and simulated performance of a new P2P reputation system,PowerTrust. the contributions are summarized in four aspects: 1. Power-law distribution of peer feedbacks: 2. Fast reputation aggregation, ranking, and updating 3. System scalability and wide applicability 4. System robustness and operational efficiency further work: 1. Coping with peer abuses and selfishness 2. Reputation system for unstructured P2P System 3. Explore new killer P2P applications 个人点评： 好文章，层次清楚，观点新颖，具有应用价值！多读！ PowerTrust A Robust and Scalable Reputation.pdf 又一篇中文翻译版： 基于幂律分布的P2P信誉评估机制.pdf Fuzzy Trust Integration for Security Enforcement in Grid Computing Shanshan Song, Kai Hwang, and Mikin Macwan IFIP International Federation for Information Processing 2004， LNCS Abstract. How to build the mutual trust among Grid resources sites is crucial to secure distributed Grid applications. We suggest enhancing the trust index of resource sites by upgrading their intrusion defense capabilities and checking the success rate of jobs running on the platforms. We propose a new fuzzy-logic trust model for securing Grid resources. Grid security is enforced through trust update, propagation, and integration across sites. Fuzzy trust integration reduces platform vulnerability and guides the defense deployment across Grid sites. We developed a SeGO scheduler for trusted Grid resource allocation. The SeGO scheduler optimizes the aggregate computing power with security assurance under fixed budget constraints. The effectiveness of the scheme was verified by simulation experiments. Our results show up to 90% enhancement in site security. Compared with no trust integration, our scheme leads to 114% improvement in Grid performance/cost ratio. The job drop rate reduces by 75%. The utilization of Grid resources increased to 92.6% as more jobs are submitted. These results demonstrate significant performance gains through optimized resource allocation and aggressive security reinforcement. 1. Introduction fuzzy logic the interaction between two Grid Resource sites The organization of this paper 1) Section 2 present author's distributed security architecture at USC GridSec project. 2) Section 3 introduces the fuzzy logic for trust management. 3) Section 4 describes the process of fuzzy trust integration. 4) Section 5 introduces the optimized resource allocation scheme. 5) Section 6 reported all experimental results. 2. GridSec Project for Trusted Grid Computing Virtual Private Networks (VPNs) vs PKI 3. Fuzzy Logic for Trust Management Two advantages of using fuzzy-logic to quantify trust in Grid applications are: (1) Fuzzy inference is capable of quantifying imprecise data or uncertainty in measuring the security index of resource sites. (2) Different membership functions and inference rules could be developed for different Grid applications, without changing the fuzzy inference engine. the trust index: job success rate and self-defense capability Fuzzy inference : a process to assess the trust index in five steps: (1) Register the initial values of the success rate Φ and defense capability Δ. (2) Use the membership functions to generate membership degrees for Φ and Δ. (3) Apply the fuzzy rule set to map the input space (Φ - Δ space) onto the output space (Γ space) through fuzzy ‘AND’ and ‘IMPLY’ operations. (4) Aggregate the outputs from each rules (5) Derive the trust index through a defuzzification process. fuzzy inference rules 4. Trust Integration Across Grid Resource Sites trust index trust vector the new trust index from the old value and present stimulus Trust update and trust propagation processes are specified in Algorithms 1 and Algorithm 2, respectively two simulation terms : the trust index_TTL and trust vector_TTL 5. Optimization of Trusted Resource Allocation the goal: Based on the fuzzy trust model, we present below in Algorithm 3 the SeGO scheduler for optimized Grid resource allocation. A job is submitted with the descriptor Job = (W, D, T, B), representing the workload, execution deadline, minimum trust, and budget limit Algorithm 4 specifies the trust integration process, in which n jobs are mapped to m sites 6. Simulation Results on Trusted Grid Resource Allocation 7. Conclusions and Suggestions for Further Research 个人点评： 这篇文章主要是基于trust model的 Grid resource allocation. 算法描述方式可借鉴 Fuzzy trust integration for security enforcement in grid computing.pdf
Read this article first. Do Law Schools Give Students a Raw Deal? ps. I heard some lawyers telling me the same story: I applied to both law schools and medical school. I did not get into a medical school. So, here I am.
Huilin Remonstrance: The Basic Law of the U niverse By Yonghe Zhang The basic law of the universe: movement, decomposition, continuous, harmonious and cycle. By movement and decomposition, decomposition and continuous, continuous and harmony, harmony and cycle. The particle movement is of kinetic energy, the decomposition of particle nature, the continuous of wave property,the harmony ofpotential energy, and the cycle of the orbitals, When the decomposition and continuous discord, the universe formed the macro world, the basic law of the universe is in linewith classical mechanics: a = F/m. When the decomposition and continuous harmony, the universe formedthe microscopic world, the rules consistent with wave mechanics: H=E. The w ave function describes a particle with a mass m and the potential energy Ze 2 /r due to the interaction with the external force, and this wave function obeys the Schrodinger equation: -h 2 2 / 8 2 m - Z e 2 /4 ? 0 r = E This potential energy Z e 2 /r formed by the interaction with the nuclear charge Z e 2 and constraining by localizing r is defined as the Ionocovalent Duality: I ( I z )C(n*r c -1 ) = n*(I av /R) ½ r c -1 where Z* is Zhang effectivenuclearcharge: Z*=n*( I z /R) ½ The universe originated from the microcosm, and follows the microcosm. The inevitability of the macrocosm is a part of the probability of the microcosm .
标签: Law
