New Generation Of Artificial Intelligence Research Articles

Use of Social Media and Artificial Intelligence Tools by Online Doctoral Students

Our paper aims to explore how doctoral EdD students in their thesis stage made use of digital technologies, social media (SM), and artificial intelligence (AI) tools. In this study, AI does not involve data on the use of the new generation of AI, which has been introduced in more recent years after this study took place. This paper refers to a 2nd stage qualitative analysis of semi-structured interviews collected from research undertaken in 2018 into student use of digital technologies in an online professional doctorate programme. The original study utilised an exploratory case study approach, an online survey (n = 28), and a series of semi-structured interviews (n = 9). This study will add further qualitative findings and perspectives to those that emerged in the previous study. This study will help to provide new insights into the interview data that was used to inform the initial paper resulting from the research in 2018. We argue that the unique characteristics of online doctoral students as both individuals and learners determine the popularity of some digital tools and that, in order to make the best use of the full range available they need to develop new skills and a better understanding of the pedagogy associated with those digital tools and the value they can add to an educational context. This can be achieved through the provision of more systematic coaching and support systems. This in turn will contribute to enhancing students’ feelings of belonging to a real academic learning community and their self-confidence and autonomy as online learners in general and in their performance in a Viva in particular.

Development of New Generation of Artificial Intelligence in China: When Beijing’s Global Ambitions Meet Local Realities

ABSTRACT How did China become one of the leaders in AI development, and will China prevail in the ongoing AI race with the US? Existing studies have focused on the Chinese central government’s role in promoting AI. Notwithstanding the importance of the central government, a significant portion of the responsibility for AI development falls on local governments’ shoulders. Local governments have diverging interests, capacities and, therefore, approaches to promoting AI. This poses an important question: How do local governments respond to the central government’s policies on emerging technologies, such as AI? This article answers this question by examining the convergence or divergence of central and local priorities related to AI development by analysing the central and local AI policy documents and the provincial variations by focusing on the diffusion of the New Generation Artificial Intelligence Development Plan (NGAIDP) in China. Using a unique dataset of China’s provincial AI-related policies that cite the NGAIDP, the nature of diffusion of the NGAIDP is examined by conducting content analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA). This study highlights the important role of local governments in China’s AI development and emphasises examining policy diffusion as a political process.

Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions

Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.

Fully Photon Controlled Synaptic Memristor for Neuro‐Inspired Computing

AbstractThe emerging optoelectronic memristive synapses having the advantages of both optics and electronics exhibit a great potential in neuro‐inspired computing, which is a new generation of artificial intelligence. Herein, a light stimulated synaptic memristor (LSSM) based on ZnO/Zn2SnO4 heterostructure is prepared with the characteristics of reversibly tunable conductance states by varying the wavelength of the incident light. The synaptic feature of this fully photon controlled memristive synapse is revealed by potentiation and depression behaviors stimulated by violet and red light pulses, respectively. Similar to biological brain, the device demonstrates the dynamic learning and forgetting behavior. All‐optically driven and bio‐vision inspired image processing function such as contrast enhancement is exemplified. The international Morse code for Arabic numerals (0–9) is also successfully conveyed by patterned light pulses and suggests the device's potential in the field of optical wireless communication for human–machine interface. Classical Pavlovian conditioning (associative learning) is successfully demonstrated through visible light induction. Finally, the device can realize the recognition application of Zalando's article image through the simulation based on Hopfield neural network (HNN). This work provides a promising approach toward optoelectronic neural systems and human–machine interaction technologies.

China’s Techno-Economic Statecraft Amid US-China Strategic Rivalry: AI and the “New Whole-State System”

In 2017, the Chinese government launched the State Council’s Program for Developing the New Generation of Artificial Intelligence (AI), which aimed to build China’s first-mover advantage by fully mobilizing and tapping its national R&D resources in both the public and private sectors. This article argues that the AI example illustrates how China resorts to a new “whole-state” approach to keep pace with the United States in key high-tech development. Such economic statecraft in advancing AI has allowed Beijing to generate faster effects in economic, social, and security dimensions. Beijing has made the application of AI in the People’s Liberation Army (PLA) war preparation a state policy in order to compete with Washington in developing emerging technologies on an even footing. Particularly, the PLA regards the development of unmanned combat aerial vehicle equipment as the point of entry into the AI race against other top powers. Amid the US policy to restrict China’s advancement in AI, China’s new whole-state-system approach in AI R&D will continue to mobilize all available human and material resources to sustain the long and fierce competition with the United States.

Security Oriented Analysis and Design Framework for Cyber - Physical - Social Enterprise

With the development of industrial technology revolution, information technology revolution and management reform, artificial intelligence plays an increasingly important role in the field of enterprise management and control. Because of the learning ability of the new generation of artificial intelligence based on big data and machine learning, artificial intelligence is increasingly showing the characteristics of human intelligence in perception, decision-making, control, optimization, etc. The basic state of binary opposition between people and industrial systems in traditional manufacturing enterprises is increasingly replaced by the ternary integration of People, Cyber and Physical Systems. Enterprises are developing into Cyber Physical Social Enterprises (CPSE). Because machine learning methods are un-robust and unexplainable, it is necessary to identify and evaluate risks of Cyber System in advance when Cyber System is to play the role of decision-making and control in the enterprise. It is also important to systematically analyze and deal with the possible negative impact of Cyber System or AI. On the basis of CPSE architecture, this paper studies its security oriented analysis and design technology, puts forward the framework of CPSE security analysis and design, and maps this framework to the general analysis and design methodology to demonstrate its applicability.

Education Sustainability for Intelligent Manufacturing in the Context of the New Generation of Artificial Intelligence

With the continuous breakthrough and innovation of artificial intelligence technology, the demand for diversified and multi-level compound intelligent manufacturing talents keeps growing. However, the current pace of intelligent manufacturing talent education in colleges and universities is still difficult to keep up with the advances in science and technology in the context of the new generation of artificial intelligence. This work conducted visual research of the literature on artificial intelligence in the field of manufacturing. All the literature was retrieved from the Web of Science Core Collection and divided into three periods (1979–1994, 1995–2007 and 2008–2021) according to the fluctuation of literature volume. Bibliometric and content analysis of the related literature during these periods were conducted to track the hotspots and trend of artificial intelligence in the field of manufacturing. The results showed that the internet of things, deep learning, cyber physical systems and smart manufacturing have been the new research hotspots. Finally, a series of suggestions were given for the sustainable education of intelligent manufacturing talents in the context of the new generation of artificial intelligence. This work may provide references for the construction of sustainable education systems for intelligent manufacturing talents in the context of the new generation of artificial intelligence.

Intelligent Multi-AUG Ocean Data Collection Scheme in Maritime Wireless Communication Network

The new generation of wireless networks is strengthening ocean development capabilities. The deep-sea detection ability of the offshore platform is being improved by Ocean Internet of Everything (OIoE) system formed by various equipment. This paper studies the application of a new generation of Artificial Intelligence (AI) wireless communication technology in marine monitoring platforms. The purpose is to solve the problem of high data redundancy and high energy consumption in multi-Automatic Underwater Glider (AUG) monitoring. In response to these problems, this paper proposes a matrix completion-based Sampling Points Selection joint Multi-AUG Path Planning (SPS-MAPP) scheme, which includes AUGs path planning and collection information matrix completion. In parts of AUGs path planning, the SPS-MAPP designed a multi-AUG trajectory optimization method using intermittent satellite communication. It proposes a reinforcement learning reward rule based on the idea of artificial potential field, which can effectively increase the coverage of AUGs trajectory. In parts of information processing, the SPS-MAPP proposes a 3-D matrix completion method, which searches for key nodes and edge nodes by designing regular sample points and operators to recover the entire space information. The experimental results show that the proposed SPS-MAPP scheme can effectively improve the subsea monitoring capability of multi-AUG.

Discussion on Innovative Methods of Higher Teacher Education and Training Based on New Artificial Intelligence

In Chinese colleges, teachers’ teaching level and proficiency in business knowledge are an important part of maintaining the knowledge system of colleges and universities. Therefore, in order to improve the teaching level of Chinese colleges and improve the knowledge reserve of college students, we should spare no effort to develop and improve the professional level of each teacher. The research on teacher training mode based on the development of teachers’ teaching ability is of great significance to the development of colleges and the improvement of teaching quality. The new generation of artificial intelligence plays a pivotal role in the training of college teachers. This paper randomly selects 897 undergraduate teachers from 10 private colleges in a certain region of China who participate in higher teacher training for research. It mainly studies the application effect of several typical artificial intelligence technologies in evaluating higher teacher education and training. Specifically, the paper uses artificial intelligence as a carrier to predict the performance indicators of the three main modules involved in higher teacher education and training. The prediction results of several artificial intelligence techniques show that the support vector machine method based on immune algorithm optimization corresponds to the largest coefficient of determination and the smallest root mean square error. Therefore, it predicts the best. Furthermore, the comparison results between the predicted data and the measured data show that the artificial intelligence technology has a better prediction effect on the three module indicators. At the same time, there is a good exponential function relationship between the prediction data corresponding to the two main modules of organization and personnel.

Identification method for collective consensus mechanism based on inverse reinforcement learning

<p indent="0mm">Collective intelligence, an important topic of the new generation of artificial intelligence, is a significant way to solve large-scale and complex problems in an open environment. It is crucial in other branches of artificial intelligence. Agents interact and evolve in response to the consensus mechanism to reach a group consensus. Identifying the consensus mechanism is essential for building and comprehending the collective intelligence system. Traditional consensus mechanism modeling methods require many simplified assumptions, and meeting the challenge of complex collective intelligence systems is difficult. A method for identifying consensus mechanisms based on data should be developed. In this paper, the consensus mechanism’s identification problem is transformed into an inverse reinforcement learning problem for the collective intelligence system. We proposed inverse reinforcement learning methods for collective systems and evaluated them in two tasks. The results indicate that the proposed methods can identify the policy function and the reward function of the collective system.

Training Mode of Innovative Accounting Talents in Colleges Using Artificial Intelligence

With the rise of big data revolution, a new generation of artificial intelligence (AI) is gaining momentum and the AI era will come strongly. However, the accounting profession is also showing a booming trend in colleges and universities, which, as the main base of higher education talent training, also undertake a considerable part of the task of training accounting talents. As traditional accounting is gradually replaced, finance professionals are being forced to gradually change to management talents. In the information era, how to realize the transformation of talent cultivation mode of financial management professionals, how to use modern technology to realize the deep integration of AI and education teaching, and how to realize the innovation of intelligent classroom teaching mode are the challenges facing the innovation of applied talent cultivation mode in colleges and universities. The article combines the changes brought by AI to the accounting industry, analyzes the current situation of accounting talent cultivation mode in colleges and universities, sorts out the challenges brought by AI to college accounting talent cultivation, and proposes that the college accounting talent cultivation mode should be further improved in terms of talent cultivation structure, cultivation plan and goal, and cultivation ideas and methods. The experimental results show that the average application time of AI basic scheduling algorithm is 1.47 h shorter than that without AI basic scheduling algorithm, which can not only increase the total number of tasks in a planned and controlled way but also ensure the effective reduction of the whole task running time in the platform. Therefore, the AI-based innovative accounting talents cultivation model in colleges and universities, exploring an excellent new model for cultivating innovative talents in accounting professions, enables the innovative accounting talents cultivated in schools to meet the needs of society and enhance their irreplaceability.

D3AdvM: A direct 3D adversarial sample attack inside mesh data

Deep learning and neural networks are being extended to geometric design and computation. Recent studies show that deep neural networks are vulnerable to adversarial samples. However, for 3D mesh adversarial samples, the most related studies actually attack the 2D victim networks, in which they have to project 3D objects to 2D images. In this paper, we present D3AdvM (Direct 3D adversary for mesh) to directly generate adversarial samples inside mesh data. Specifically, we propose two adversary generation approaches: vertex-based and edge-based. The first one, 3DVP (3D Vertex-based Perturbation), skillfully searches the optimized vertices positions based on opposite gradient fitness. The second one, KES (Key Edge-based Selection), carefully collapses the key feature edges according to importance of edge feature. Thus, our approaches avoid 3D/2D projections and approximation errors. Also, our approach can reduce the computation overhead. In addition, the proposed D3AdvM can control the number of perturbed vertices for real-world engineering designs and applications. Extensive experiments show that the generated 3D meshes are effective to attack classification networks. Furthermore, we evaluate the transferability, in which D3AdvM can attack both mesh-based networks and point cloud-based networks as victim networks. Our findings could benefit 3D geometry design based on the new generation of artificial intelligence and big data.

Prophet Inequality for Bipartite Matching: Merits of Being Simple and Nonadaptive

We consider the Bayesian online selection problem of a matching in bipartite graphs, that is, the weighted online matching problem where the edges arrive online and edge weights are generated from a known distribution. This setting corresponds to the intersection of two matroids in the work of Kleinberg and Weinberg [ 40 ] and Feldman et al. [ 27 ]. We study a simple class of nonadaptive policies that we call vertex-additive policies. A vertex-additive policy assigns static prices to every vertex in the graph and accepts only those edges whose weight exceeds the sum of the prices on the edge endpoints. We show that there exists a vertex-additive policy with the expected payoff of at least one-third of the prophet’s payoff and present a gradient descent algorithm that quickly converges to the desired vector of vertex prices. Our results improve on the adaptive online policies of Kleinberg and Weinberg and Feldman et al. for the intersection of two matroids in two ways: our policy is nonadaptive and has a better approximation guarantee of 3 instead of the previous guarantees of 5.82 in Kleinberg and Weinberg and 5.43 in Feldman et al. We give a complementary lower bound of 2.25 for any online algorithm in the bipartite matching setting. Funding: This work was supported by Fundamental Research Funds of the Central Universities in China, a Science and Technology Innovation 2030 major project [“New Generation of Artificial Intelligence,” Project 2018AAA0100903], the Shanghai Municipal Education Commission Innovation Program, and the Program for Innovative Research Team of the Shanghai University of Finance and Economics (IRTSHUFE).

On Interdisciplinary Studies of a New Generation of Artificial Intelligence and Logic

A new generation of artificial intelligence (NGAI), currently based on big data and machine learning, follows a path of connectionism. Although this path achieves huge success in data-intensive applications under closed environments, there are some bottleneck problems, including a lack of explainability, the difficulty of ethical alignment, the weakness of cognitive reasoning, etc. To address these problems inevitably involves the depiction of information from an open, dynamic and real environment and the modeling of human reasoning and explanation mechanisms. Formal argumentation is a general formalism for modeling various types of knowledge representation and reasoning in a context of disagreement, and is flexible enough to incorporate other types of knowledge for decisionmaking, such as preferences, weights, and probabilities. Meanwhile, there are various approaches for efficient computation of argumentation semantics by exploiting the locality and modularity of argumentation, and for providing explanations based on arguments and dialogues. The organic combination of formal argumentation with existing big data and machine learning techniques can be expected to break through some existing technical bottlenecks and facilitate the sustainable development of NGAI.

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded.

Analysis of Ecological Environment Evaluation and Coupled and Coordinated Development of Smart Cities Based on Multisource Data

In order to further enhance the ecological environment construction of smart cities and promote its deep integration with advanced technologies, such as the new generation of artificial intelligence and big data, this study constructs a data fusion framework for the ecological environment of smart cities driven by multisource data and constructs an ecological environment evaluation index system of smart cities in Guangzhou from 2010 to 2018. The ecological environment status of smart cities in Guangzhou is analyzed by principal component analysis, and finally the, correlation influence degree of each principal component content on the ecological environment index of smart cities is analyzed. The results show that environmental excellence ( K 1 ), environmental restoration response ( K 2 ), and environmental pollution pressure ( K 3 ) are the main components of the ecological environment in Guangzhou smart city. The environmental excellence and environmental restoration response level are relatively high, and the environmental pressure system is relatively low, in which the greenland coverage rate increases from 41.3% to 44.0%, and the urban sewage treatment rate and the decontamination rate of urban refuse increase year by year, from 73.1% to 94.6% and from 72.1% to 99.9%, respectively. The results of principal component correlation analysis show that there are interactive influences among environmental excellence ( K 1 ), environmental restoration response (K2), and environmental pollution pressure ( K 3 ). The ecological environment index of smart cities increases with the improvement of environmental excellence and environmental restoration response capacity, but gradually decreases with the increase of environmental pollution pressure. Generally speaking, improving environmental excellence and environmental restoration response will be the key to improve the ecological environment construction capacity of smart cities in the future.

Photonic Synaptic Transistor Based on P-Type Organic Semiconductor Blending With N-Type Organic Semiconductor

Neuromorphic computing, with information sensing, processing, and storage capabilities, is driving the development of a new generation of artificial intelligence (AI). Photonic synaptic devices are essential components for improving information processing efficiency owing to their advantages of a high propagation speed, low latency, and large bandwidth. In this study, a photonic synaptic transistor simply based on p-type organic semiconductor blending with n-type organic semiconductor was fabricated via simple solution process in which only light was used to control the enhancement and inhibition of the synaptic conductance. The device can simulate fundamental synaptic properties. Additionally, the coordination between light stimulation and electrical stimulation was investigated, and the results show that light-pulse stimulation is beneficial for increasing the response current simulated by electrical pulses. This work represents a significant step towards the realization of high-efficiency AI electronics.

Research on the Problems and Countermeasures of Higher Education Management Informatization Development in the Computer Internet Era

The construction of local high-level universities is an important part of the comprehensive reform of higher education in China. With the coming of the computer “Internet +” era, major strategic tasks such as cloud computing, big data and new generation of artificial intelligence have been gradually laid out, which will further achieve the goal of educational modernization and the development of a powerful educational country. Promoting the development of education informatization in the Internet era has become the top priority in the development of national education. Based on the background of high-level universities, this paper first analyses the necessity of the educational informatization development. Then, this paper puts forward some problems. Finally, some suggestions are put forward [1].

Interface, interaction, and intelligence in generalized brain–computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

AbstractNeuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh‐density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all‐optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light‐induced electron trapping and detrapping. It is found that the light‐induced multiple memconductance states are nonvolatile. Furthermore, spike‐timing‐dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.