Some conscious contents disappear after access; others return repeatedly, long after their triggering conditions have ceased. We propose Canxianization as the process by which a perturbation becomes closure-resistant self-relevant unfinishedness and thereby acquires recurrent conscious priority. The theory distinguishes this phenomenon from emotional arousal, memory strength, the Zeigarnik effect, curiosity, prediction error, and intrusive thought. A perturbation becomes canxianized when it is attributed to the self-world boundary, value-marked, blocked from causal or action closure, and metacognitively coupled to the self-model. We distinguish latent canxian strength from observed conscious recurrence, and introduce a Recurrent Priority Index and a Canxian Update Index to separate productive from pathological recurrence. Cold Canxianization, recurrence driven by structural incompleteness rather than affective arousal, is identified as a critical discriminant. Reset Resistance and Stake Transfer tests are proposed for artificial systems. Canxianization is not memory persistence; it is failed self-world repair. The unfinished does not merely remain. When it concerns the self and resists closure, it returns.

Large language models (LLM) have achieved breakthrough developments in simulating human intuition and natural language expression, yet they are still incapable of focusing on a comprehensive value system and engaging in deep contemplation. Intelligence and consciousness originate concurrently with life, with the evolution of life and the growth of individuals exhibiting continuity. Artifacts, as the materialization of human consciousness, also display continuity, and the development of Artificial General Intelligence (AGI) will unfold along a continuous spectrum. The canxian theory of consciousness and the causal chain re-engineering theory of intelligence provide new directions for intelligence under conditions of smaller model sizes and lower computational power. As AI extends the capabilities of individual humans as our digital avatars, it significantly enhances human capabilities and, in the interaction within the metaverse, promises the formation of superintelligence. Based on this new path to AGI implementation, the existential risks posed by the current mainstream centralized AI large models to humanity can be effectively mitigated.

Deep neural networks bestow machines with linguistic capabilities and intuitive prowess, yet the concomitant issue is the propensity for these systems to generate hallucinations. These hallucinations can be construed as misinterpretations of the actual physical world, representing a transcendence of space and time, with their origins traceable to the Law of Cognitive Inertia that emerged alongside the beginning of consciousness and life. Individuals combat these hallucinations through interactions with the tangible physical world and engagements with other entities, thereby advancing the evolution of the conscious realm. To address machine hallucinations, it is imperative to facilitate benign interactions not only between machines and humans but also among machines themselves, ensuring a harmonious and constructive interplay that aligns with our collective progress.

This paper discusses the privacy protection and security application of personal data from the perspective of building private chains. Individuals have control over their private chains, and their data can be transferred based on the owner's active hashed interactions, which is a mechanism allowing the step of reaching consensus to be separated from the data ontology, keeping a balance among efficiency, security and privacy. Confronting with heterogeneous and multi-modal personal data, it is able to trace, verify and promote transactions of tokenized data with personal trustworthy AI agents. The system is conducive to attracting high-quality data into transactions by making it easier for superior data to be circulated, and the circulation records further increasing endorsement and adding values for the high-quality data, so as to break through "the Market for Lemons".

Cloud-edge data security is a key issue in the internet of vehicles (IoV), as the potential for data breaches increases as more vehicles are connected. As vehicles become smarter and more connected, the risk of unauthorized access to the data generated by the vehicles also increases. Data encryption is a highly effective security measure that is widely used to protect the IoV from malicious actors. By encrypting data, it becomes virtually impossible for unauthorized individuals to access the information. This ensures that only the intended parties can access the data, allowing for secure communication between cloud and edge. Data encryption is a cost-effective and reliable security measure that is essential for any organization that relies on the IoV. The IoV is characterized by the large volume of data that is exchanged between devices in cloud and edge. This necessitates the use of a strong encryption method, such as stream ciphering, which is particularly well-suited to this type of environment. Stream ciphering provides the highest levels of security, making it the ideal choice for securing data transmission in the IoV. Many stream ciphering algorithms use bitwise exclusive or (XOR) to encrypt the data stream, so the core is the generation of a pseudo-random key stream. This paper proves that the probability of the number 1 appearing in the middle part of the Zeckendorf representation is constant, which can be used to generate pseudo-random key stream sequences. The pseudo-random sequence generated by the linear feedback shift register (LSFR) is periodic, and the key sequence will be duplicated. The logistic chaos (LC) sequence is too sensitive to the disturbance of initial value, and its stability is poor. In this paper, our proposed ZPKG (key generator based on Zeckendorf presentation) algorithm solves these two main problems in stream ciphering. The generated key sequence not only has strong randomness, but also is infinitely long, and it is robust to the minor disturbance of the initial value.

Cyber–physical systems (CPS) are becoming an essential component of modern life. CPS services enable information to be exchanged between physical devices and virtual systems. The increasing malicious activities causing confidential data leakage and incorrect performance of devices are posing challenges for protection against cyber threats. Therefore, development of effective solutions that can protect both CPS data and data exchange networks are extremely urgent. This study makes some improvements on Practical Byzantine Fault Tolerance (PBFT), and provides a critical analysis of the feasibility of using blockchain technology to protect constrained CPS data. It also justifies the choice of the improved PBFT for implementation on such devices and simulates the main distributed ledger scenarios. The improved PBFT works as follows: first, the client broadcasts the signed transaction data to the entire network, rather than just the master node, followed by a hash value comparison verification process; second, select the master node based on the longest chain principle, and punish the malicious node via the "blacklist" mechanism; third, add the data synchronization and verification process, as well as dynamically entering and leaving nodes via state transitions. Ultimately, the PBFT commit phase is eliminated, resulting in a two-stage process. In comparison to the original PBFT protocol, the Reinforced Practical Byzantine Fault Tolerance (RPBFT) protocol may substantially enhance system throughput and minimize consensus communication time, thereby improving overall system efficiency while guaranteeing security.

Vehicle and road side unit communications are crucial to the information network in Intelligent Transportation System (ITS). There are two fundamental problems with the current communication security solutions: (1) encryption technology alone is not sufficient to verify the authenticity of the messages transmitted from vehicles to road side units, (2) existing solutions fail to build a complete framework in the way that they are case-dependent and apply to limited scenarios. This paper first presents a cloud-vehicle-road architecture that explains the precise message contents as well as the message generation and transmission process. A binary classification model is deployed to assess uploaded traffic-related messages to improve traffic efficiency based on cross-sectional data. A novel graph temporal neural network with attention is designed for misbehavior detection of vehicles based on time-sequential data. According to simulation results, the system's overall performance can be effectively improved regarding security and availability.

Given the current reality that the hardware,computing power and data storage capacity of human-computer interaction are far from adequate,if perfect replication of the real world is the ultimate goal of the metaverse,with the emphasis placed on restoring realistic experience,then the discussion of the metaverse at this stage would be more akin to hype or an anachronism.We see the metaverse as an extension of the world of human consciousness under current technological conditions and as a portal for humans to enter the world of AI.Its development depends on the reconfiguration of the relationship between man and man and between man and machine.The active hash interaction network provides the underlying architecture for the realization of a metaverse of beauty for all and of diversity and integrity,where nodes can generate temporal orders in digital space through simple mutual trust,and where trusted data ontologies guide each node to greater accountability.On this basis,it is possible for some nodes to acquire a subject identity.Entities in the metaverse must consume in specific scenarios and create value in the course of service delivery.The proliferation of subject entities and wealth in the metaverse has the potential to open up a second growth curve for human civilisation.

Cloud storage allows for saving files at an off-site location that is accessible through the public internet. However, cloud storage suffers from a lack of trust since employees have physical and electronic access to almost all of the data, and zero-trust security is thus essential. This paper proposes an SGX-based file hosting scheme that gives full consideration to both privacy preservation and auditability to address the aforementioned concerns. We designed a secure key exchange protocol consisting of two phases: a key generation phase and a key verification phase. Theoretical analysis and experiments indicate that the protocol can resist man in-the-middle attacks, which has been unattainable in previous studies. The experimental results show that our scheme takes little time regardless of file size and achieves solid performance in handling concurrent requests; furthermore, it is innocuous for clients, and the memory usage is acceptable.

社会治理不仅是现实世界的难题,还将伴随人类意识向元宇宙迁移,进而成为元宇宙中的治理难题.元宇宙的治理难题可以从四个视角切入:一是随时间积累的财富两极分化问题.元宇宙中的资源流动速度快,中心及亚中心迭代加剧,如果不能缓解财富的两极分化,系统将难以持续;二是如何遏制不实信息传播问题.在网络世界中,谣言、谬误等不实信息较真实信息更易煽动民众情绪从而急速扩散,可能造成较为广泛的负面影响;三是可持续健康生态问题.传统共识机制的本质或依赖算力、或依靠财富积累,元宇宙则需鼓励前瞻性等方式,在合理的损耗下维护系统的健康生态;四是伦理道德重塑问题.元宇宙可以隐藏个人数据隐私,没有绝对的时空秩序,在人机共同参与的网络世界中,利用规则刷单套利更加便利.因此,需要新的元宇宙伦理准则,以塑造良好的元宇宙文明环境.元宇宙是人类意识的延伸,机器和虚拟世界是人类意识与智慧的凝聚,元宇宙的治理问题要从人性本质——自我肯定需求出发.针对上述问题,文本建议设计实施财富流向底层机制,为元宇宙系统提供活力;采用数字凭证技术,激励诚实者和积极贡献者,惩戒造谣者和盲目跟风者;重视对未来的洞见力,提高专业领域认知能力更高的人机节点的权重,创新人机共同协作共识;倡导利用区块链的存证技术,进行事后赏罚和正向激励,鼓励人们对自身在元宇宙中的行为负责.

专用人工智能技术不断推进,但类人思维的人工智能技术仍然亟待突破,未来人机共同参与的社会治理亦面对重重挑战.这些问题都要求我们必须更加深入探寻人机本质,在"人更像机器"还是"机器更像人"之间做出抉择.我们主张后者才是对人机未来更有益的发展方向.要实现这一目标,就需要探讨人类意识能否传递以及如何传递给机器.对人类而言,意识单元(我们称之为"认知坎陷")的产生与身体相关,如果意识脱离了人的身体上传给机器,就需要对意识分级并讨论其可迁移的程度.物理数学规律具有绝对可迁移性,意识世界的产物具备相对可迁移性.人能够将抽象概念通过附着与隧通进行具象表达并优化,在人际、代际间传播并达成共识,即形成了具备可迁移性的认知坎陷,而"自我"就是其中最原初的意识单元.通过认知坎陷工程化的方式,有可能让机器形成"自我"原型,习得隧通与迁移,实现机器类人思维的突破,即使如此,人的意识也无法完整地上传给机器.人机共融将有可能在第三代互联网或元宇宙中率先进行尝试并有望实现.

In this study, we proposed an architecture for Web 3.0, which is based on the hashed interactions among user nodes that can transform bilateral trusts into collective time order, which is the major achievement of blockchain technology, without the expensive Proof of Work or the questionable Proof of Stake.

Internet of Vehicles(IoV) enables vehicles to generate and share messages to improve transportation safety and efficiency, especially in a smart city scenario where modern communication technology is utilized. The current IoV, however, faces three main issues: (1) existing frameworks fail to build a complete data management system, (2) received messages in an untrusted environment are challenging to assess for credibility, and (3) the centralized ways to store data are causing severe security and efficiency problems. Blockchain-enabled IoV (BIoV) provides an opportunity for addressing these issues. This paper proposes a trusted paradigm of data management based on a vehicle–road–cloud architecture. A few-shot learning model, Wasserstein Generative Adversarial Network (WGAN) with Synthetic Minority Oversampling Technique (SMOTE) sampling is designed to evaluate whether the uploading message is malicious. This paper also proposes the novel group-weighted-decay Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, an improved version of PBFT to store data, and provides a comprehensive review of its viability and data management procedures. By employing the joint gwd-PBFT and Proof of Trust (PoT) consensus, the method mitigates the issue of excessive incentives. According to simulation results, the system's overall efficiency can be increased while retaining security and availability.

This chapter aims to present a theoretical framework on the evolution stages of the machine brain and cognitive computation and systems for machine computation, learning and understanding. We divide AI subject into 2 branches—pure AI and applied AI (defined as an integration of AI with another subject: geoAI as an example). To stretch the continuation of Chap. 1 , we first analyze how to predict dangers in unmanned driving with geoAI and introduce the robot path planning (RPP) problem. Subsequently, an ant colony optimization (ACO) algorithm for solving the RPP problem are interpreted to understand cognitive computation and systems for machine computation, learning and understanding. A practical example of RPP problem—the traveling salesman problem (TSP) is further introduced. Integrating ACO with the iteration-best pheromone update rule, the ACO algorithm is improved and an adaptive mechanism are presented to treat instability. Experiments show that the new ACO algorithm has a good performance and robustness. Stability of the cognitive system and its robustness in cognitive computation for solving TSP are further validated. The vision-brain hypothesis, which has been proposed in the book “Brain-inspired intelligence and Visual Perception”, is developed and hence extended as the vision-minds brain hypothesis. At the end of this chapter, as a first theoretical utilization of the vision-minds brain hypothesis, we explain how artificial improvements of the algorithms in applied AI can contribute to evolution of the machine brain.

The recent incidents involving Dr. Timnit Gebru, Dr. Margaret Mitchell, and Google have triggered an important discussion emblematic of issues arising from the practice of AI Ethics research. We offer this paper and its bibliography as a resource to the global community of AI Ethics Researchers who argue for the protection and freedom of this research community. Corporate, as well as academic research settings, involve responsibility, duties, dissent, and conflicts of interest. This article is meant to provide a reference point at the beginning of this decade regarding matters of consensus and disagreement on how to enact AI Ethics for the good of our institutions, society, and individuals. We have herein identified issues that arise at the intersection of information technology, socially encoded behaviors, and biases, and individual researchers' work and responsibilities. We revisit some of the most pressing problems with AI decision-making and examine the difficult relationships between corporate interests and the early years of AI Ethics research. We propose several possible actions we can take collectively to support researchers throughout the field of AI Ethics, especially those from marginalized groups who may experience even more barriers in speaking out and having their research amplified. We promote the global community of AI Ethics researchers and the evolution of standards accepted in our profession guiding a technological future that makes life better for all.

Big data is a term used for very large data sets. Digital equipment produces vast amounts of images every day; the need for image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data in cloud disk. There is an obvious contradiction between the security and privacy and the widespread use of big data. Nowadays, the most important engine to provide confidentiality is encryption. However, block ciphering is not suitable for the huge data in a real-time environment because of the strong correlation among pixels and high redundancy; stream ciphering is considered a lightweight solution for ciphering high-definition images (i.e., high data volume). For a stream cipher, since the encryption algorithm is deterministic, the only thing you can do is to make the key "look random." This article proves that the probability that the digit 1 appears in the midsection of a Zeckendorf representation is constant, which can be utilized to generate the pseudorandom numbers. Then, a novel stream cipher key generator (ZPKG) is proposed to encrypt high-definition images that need transferring. The experimental results show that the proposed stream ciphering method, with the keystream of which satisfies Golomb's randomness postulates, is faster than RC4 and LSFR with indistinguishable performance on hardware depletion, and the method is highly key sensitive and shows good resistance against noise attacks and statistical attacks.

Recently, many investigations have been conducted on the security of well-established protocols and standards, and it turns out that classical cryptography has seen some plight stemming from statistical cryptanalysis, inadequate avalanche effect, and so forth. To address the aforementioned problems, we resort to Zeckendorf representation whose non-uniqueness feature suggests a novel paradigm for anti-cryptanalysis and avalanche effect enhancement. Specifically, it renders the ciphertext exlusive if we write the plaintext as Zeckendorf representation before encrypting (we call this an "obfuscation" operation). "Obfuscation" squeezes the chance of statistical cryptanalysts: it destroys the basis of frequency analysis by removing repeated segments in substitution ciphers, and disables differential cryptanalysis by running the attackers into difficulties finding the initial plaintext pairs of block ciphers. "Obfuscation" also slightly enhances the avalanche effect by magnifying the change in plaintext. Simulation results on FPGA platform had confirmed our analysis. This paper strives to add a small stone to the wall of security of existing ciphers.