Exploring the Determinants of Artificial Intelligence (AI) Literacy: Digital Divide,Computational Thinking,Cognitive Absorption

To effectively utilize artificial intelligence (AI)-based technologies such as ChatGPT and realize their novel ethical issues, individuals must have a variety of knowledge and skills about AI. Such knowledge and skills have led to the emergence of AI literacy. Despite the importance of AI literacy in everyday life, little is known about its determinants. To better understand the determinants of AI literacy, we attempted to build a research model relying on previous research and different theoretical frameworks. The model incorporated digital divide, cognitive absorption, and computational thinking. As a major finding from the current study, computational thinking was found to be a significant determinant of AI literacy, which facilitate using, recognizing, and evaluating AI-based technologies. Moreover, we found out that individuals with physical access to information and communication technologies (ICTs) are more expected to use and recognize AI. Also, motivation and skills in using ICTs enable individuals to better evaluate the outcomes of AI-based technologies. The findings also showed that convenient access to ICTs contributes to a deep involvement with AI-based technologies in the use. Further, individuals with higher motivation and skills to use AI technologies are likely to have a pleasant experience after using these technologies.     

AI as a friend or assistant: The mediating role of perceived usefulness in social AI vs. functional AI

Advents of new technology have transformed how we currently view and use artificial intelligence (AI). Originally, AI was first developed to assist humans to complete tasks, but AI now takes on more social roles, such as functioning as a companion. However, little is known about how individuals view these different types of AI. Thus, the present study conducted an online experiment to explore people’s perceptions about social AI vs. functional AI. Primary results suggest that individuals have more positive attitudes toward functional AI than social AI. Perceived usefulness of AI is found to have a mediation effect, suggesting functional AI, compared to social AI, leads to stronger perceived usefulness, which consequently fosters more positive attitudes and stronger perceived realism of AI. The results collectively suggest meaningful implications for human-AI communication and human–machine communication research.     

Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives

Artificial intelligence (AI) is becoming increasingly important in all domains of life. Therefore, it is crucial to understand individuals’ attitudes towards AI. This article investigated attitudes toward AI through two studies that are based on the self-determination theory and basic psychological needs (autonomy, competence, and relatedness). Study 1 used cross-sectional samples of adult populations aged 18–75 from Finland (N = 1,541), France (N = 1,561), Germany (N = 1,529), Ireland (N = 1,112), Italy (N = 1,530), and Poland (N = 1,533). Study 2 was based on a longitudinal two-wave sample of adults aged 18–80 from Finland (N = 828). Based on the robust regression analyses, Study 1 found that fulfillment of basic psychological needs was associated with higher AI positivity and lower AI negativity across Europe. According to the Study 2 results, based on hybrid multilevel regression models, autonomy and relatedness increased AI positivity and decreased AI negativity over time. The results provide robust evidence on the role of self-determination in attitudes towards AI. Self-determination is an important factor in AI acceptance and is becoming increasingly important considering the rapid development and adoption of AI solutions.     

Performance Criteria for Spread Spectrum Communications

The criteria for antijam (AJ) and anti-intercept (AI) systems are described, in terms of the appropriate action taken by the jammer or interceptor. Avoiding pseudonoise (PN) sequences which can be partially or totally predicted is a foremost criteria, especially for AJ, and use of nonlinear feedforward logic (NFFL) with long-period linear maximal sequences appears attractive. A frequency-hopping (FH) system must anticipate a multitone jamming signal, and an error-control code is necessary. A novel method for generating a multitone signal using repeating maximal sequences is described. PN systems must anticipate a tone jammer, and now an errorcontrol code may be needed to assure that sporadic PN sequence correlation with the tone does not reduce the processing gain. Any AI system must anticipate that the interceptor may, if advantageous, integrate his decision energy over many communicator symbols (up to the message length). Now spreading both in frequency and in time may be valuable. Finally, the use of an automatic adaptive data rate is suggested to realize flexibly either AJ or AI objectives.     

High‐precision attitude determination and control system design and real‐time verification for CubeSats

This paper presents the design and real‐time verification of a high‐precision and low‐cost attitude determination and control system (ADCS) for CubeSat based on a micro‐electro‐mechanical (MEMS) gyroscope. The CubeSat new missions require accurate and sophisticated ADCS with attitude drift adjustment. Moreover, designing an effective ADCS for the CubeSat poses a difficult challenge. The satellite comprises of a two‐unit CubeSat, which denotes that the ADCS is designed with small size, tight mass, and energy limitations. The ADCS has been enhanced in the former few years from fairly small resolution of 10 to around 0.6 m. This attitude drift, if not properly compensated, will cause a slow attitude information loss as the error in attitude rises between the actual and estimated. To correct the attitude adrift, the proposed system utilizes a MEMS gyroscope sensor which offers a comparative attitude to the Kalman filter for estimated attitude update. Real‐time verification and validation for the ADCS are performed through Matlab/Simulink environment and lab testing to prove the efficacy of the proposed system. Simulation of the ADCS shows accurate results with error of no more than 1°.     

基于涵化理论视角对人工智能智商和情商态度的研究

人工智能已逐步进入生活中的各大领域,其数据分析与处理能力受到了人们的普遍认同,但人工智能的服务满意度却不尽如人意,这种现象部分源于人工智能产生的早期,虽然人工智能擅长计算,但缺乏情感。文中围绕人工智能情感的产生,结合涵化理论探究社会媒体认为的人工智能缺乏情商的背后原因,为打破AI发展的瓶颈提供解决方案。     

“OK,Google, why do I use you?” Motivations,post-consumption evaluations,and perceptions of voice AI assistants

Voice-activated, artificial intelligence–based assistants (voice AI assistants) have become an integral part of everyday life because they can be easily activated to complete numerous tasks. However, little is known about what motivates people to use them or how these motivations influence users’ post-consumption evaluations and perceptions. This study develops measures to capture uses and gratifications related to voice AI assistants. It identifies five primary motivations for use—social interaction, personal identity, conformity, life efficiency, and information. Results show that the utilitarian motivations of life efficiency and information influence all post-consumption evaluations and behavioral intentions positively (i.e., users’ attitudes, satisfaction, and intentions to continue using voice AI assistants). However, social motivations are also important. Social interaction and conformity motivations also influence user satisfaction, and the conformity motivation shapes individuals’ intentions to continue using voice AI assistants. The findings further demonstrate that users’ motivations influence perceptions of their voice AI assistants’ roles. In keeping with the “Computers Are Social Actors” (CASA) paradigm, users motivated by social interaction are likely to perceive a voice AI assistant as socially attractive and as a friend, whereas users motivated by life efficiency are likely to perceive it as an assistant. Users motivated by information are more likely to perceive a voice AI assistant as technology, while those motivated by social interaction are less likely to do so. The implications of these findings are discussed, and recommendations for future research are provided.     

机制主义:人工智能的统一理论

长期以来,学术界曾经分别从智力系统的结构、功能和行为三个不同的侧面分析和模拟人的智力过程,取得了众多成果,形成了人工智能理论的结构主义、功能主义和行为主义三大体系.这些研究也留下许多问题,而且互相缺乏沟通,常常发生"孰优孰劣"的争论.本文试图提出智能生成的"机制主义",希望从智能生成的共性机制入手探讨智能的本质.研究发现:在一般情形下,智能生成机制表现为"信息-知识-智能的转换",由此引出了"知识理论";特别有意义的是,结构主义、功能主义、行为主义三者可以在"机制主义"框架下得到和谐完美的统一.这可能为智能科学技术的发展提供新的理解,带来新的机会.     

速度拖引干扰和杂波背景下脉冲多普勒雷达目标跟踪算法

针对在速度拖引干扰和杂波背景下脉冲多普勒(PD)雷达无法精确跟踪目标的问题,该文提出基于双模型(DM)和幅度信息(AI)的目标跟踪算法。分别建立基于位置、幅度量测的跟踪模型和基于位置、速度、幅度量测的跟踪模型。两个模型均使用基于幅度信息的概率数据互联(AI-PDA)尽可能地降低杂波的影响,然后使用常规方法进行滤波估计。若没有速度拖引干扰,则两个模型估计具有位置和速度上的相关性;若存在干扰,由于速度量测是虚假的,则两个模型估计不具有相关性。据此,进行卡方检验(chi-square test),分析影响检验结果的因素,进而确定最终的估计结果。仿真验证了该算法的有效性。     

基于云原生服务网格的AI模型部署方案

人工智能模型训练完成后并不适合直接运行在互联网生产环境,需要对其进行一定的封装和部署。探讨基于容器和云原生服务网格对AI模型进行自动封装和部署,以微服务形式对外提供Restful API,能够提高模型的兼容性和部署灵活性。封装过程采用适用多框架的通用AI模型代码打包方法,解决AI模型运行环境配置问题;通过部署在云原生平台由服务网格进行管理,实现AI模型从实验室开发到互联网生产环境高效应用的自动化实时流转。     

Futures of artificial intelligence through technology readiness levels

Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are its depth, breadth and timelines. To answer them, not only do we lack the tools to determine what achievements will be attained in the near future, but we even ignore what various technologies in present-day AI are capable of. Many so-called breakthroughs in AI are associated with highly-cited research papers or good performance in some particular benchmarks. However, research breakthroughs do not directly translate into a technology that is ready to use in real-world environments. In this paper, we present a novel exemplar-based methodology to categorise and assess several AI technologies, by mapping them onto Technology Readiness Levels (TRL) (representing their depth in maturity and availability). We first interpret the nine TRLs in the context of AI, and identify several categories in AI to which they can be assigned. We then introduce a generality dimension, which represents increasing layers of breadth of the technology. These two dimensions lead to the new readiness-vs-generality charts, which show that higher TRLs are achievable for low-generality technologies, focusing on narrow or specific abilities, while high TRLs are still out of reach for more general capabilities. We include numerous examples of AI technologies in a variety of fields, and show their readiness-vs-generality charts, serving as exemplars. Finally, we show how the timelines of several AI technology exemplars at different generality layers can help forecast some short-term and mid-term trends for AI.     

30 GHz SiGe active inductor with voltage controlled Q

An active inductor (AI) based on a cascade gyrator for 30 GHz applications implemented with a 0.25 μm in SiGe technology is presented. The gyrator converts not only a key capacitor into an inductor, but also an added resistor, into a negative resistor. This gyrator-RC has its losses compensated by the negative resistor improving the active inductor Q factor. Changing the bias voltage and current allows to obtain a variable AI. A study of a cascade gyrator AI topology is done to understand the circuit behavior and key elements. For this purpose, an AI impedance model is introduced and discussed. An improved AI with the added resistor replaced by a voltage controlled mosfet resistor is proposed. This extra control voltage allows the variable AI quality factor fine tuning. Schematic and circuit extracted from layout simulations are presented, and compared with the measured results of two prototypes of the AIs (one with a fixed resistor and other with a voltage controlled resistor). A prototype of a high pass filter using the AI with fine Q control was fabricated. Non-linear simulations for different input signal levels were performed and compared with measurements. Also discussion on the non-linear models accuracy is performed.     

What factors contribute to the acceptance of artificial intelligence? A systematic review

Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears dispersed and lacks systematic synthesis, limiting our understanding of user acceptance of AI technologies. To address this gap in the literature, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and meta-Analysis guidelines using five databases: EBSCO host, Embase, Inspec (Engineering Village host), Scopus, and Web of Science. Papers were required to focus on both user acceptance and AI technology. Acceptance was defined as the behavioural intention or willingness to use, buy, or try a good or service. A total of 7912 articles were identified in the database search. Sixty articles were included in the review. Most studies (n = 31) did not define AI in their papers, and 38 studies did not define AI for their participants. The extended Technology Acceptance Model (TAM) was the most frequently used theory to assess user acceptance of AI technologies. Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted behavioural intention, willingness, and use behaviour of AI across multiple industries. However, in some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI, no matter the perceived usefulness or perceived ease of use. Given that most of the methodological approaches present in the literature have relied on self-reported data, further research using naturalistic methods is needed to validate the theoretical model/s that best predict the adoption of AI technologies.     

人工智能(AI)在电力系统中的应用

简要地介绍了人工智能技术的基本概念,并指出其在电力系统中的应用范围。对专家系统、人工神经网络、模糊理论、遗传算法等人工智能技术的基本概念进行了简单的介绍,并从实用化的观点对他们在电力系统故障诊断中的应用特点、存在问题进行分析,最后指出综合运用多种人工智能技术是电力系统中的人工智能技术应用的最新发展动向,并提出了有针对性的建议。     

The Effects of Three Types of Profane Language in Persuasive Messages

The present investigation was concerned with the effect of using profanity in a communication on receiver attitude change and speaker credibility ratings. Results of the investigation found no support for the superiority of a persuasive communication using profanity of a religious, excretory, or sexual nature over the same communication devoid of such profanity. It was found, however, that if one of the above three types of profanity is included in a communication, greater attitude change can be expected to occur if the communicator is a female using any one of the three profanity-types mentioned than if the communicator is a male using the same types of profanity. Finally, it was found that using profanity in a communication generally has a detrimental effect on the perceived credibility of the communicator.     

Spatial Velocity Distributions in the Ascending Aorta of Healthy Humans and Cardiac Patients

Blood velocity profiles in the human ascending aorta were assessed with the aid of ultrasonic Doppler echocardiography. To this end, the transducer was placed in the suprasternal notch, and the spatial velocity profiles along an axis passing through the center of the aortic cross section were recorded by a multigate Doppler instrument. The profiles are analyzed with respect to characteristics independent of the angle of incidence and the cross sectional area. Data from 10 healthy individuals, 10 patients with hypertrophic obstructive cardiomyopathy (HOCM), and 10 patients with severe aortic insufficiency (AI) are compared. Five instantaneous profiles recorded at different times during systole and the temporal average of all profiles recorded during the entire cardiac cycle at 16 ms intervals are examined. Considerable differences between the three groups of subjects are observed visually as well as quantitatively in terms of specific parameters. The representation of the velocity maps in the form of contour graphs is particularly incisive. The results demonstrate that the temporal velocity patterns measured depend, in general, on both the disease and the location of the sampling volume within the aortic lumen. Reliable aortic volume flow rate measurements may have to be based on a method which takes into account the velocity at every point of the entire vascular cross section of patients with HOCM or AI.     

一种电感值和Q峰值可相互独立调谐的高线性有源电感

设计了一种电感值和Q峰值可相互独立调谐的高线性有源电感。该电感主要由跨导增强模块、互补共源级模块以及单端负阻模块构成。其中,跨导增强模块不仅可以作为正跨导器,并且可实现对电感值的大范围调谐；互补共源级模块不仅可以作为负跨导器,并且可改善有源电感的线性度；单端负阻模块不仅提高了Q值,并且补偿由电感值的调谐导致的Q峰值的变化。最终,通过以上模块的相互配合及其外部端口电压的协同调控,改善了有源电感的线性度,而且实现了在同一频率下Q峰值相对于电感值可大范围独立调谐以及在不同频率下电感值相对于Q峰值可大范围独立调谐的优秀性能。验证结果表明,该有源电感电感值的-1 dB压缩点为-7 dBm；在2.07 GHz的频率下,Q峰值可从240调节到1573,而电感值从11.89 nH仅变化到12.11 nH；在0.989 GHz、2.070 GHz和3.058 GHz的不同频率下,取得了493.7、501.2和508.4的高Q峰值,变化率仅为3%,而相应频率下的电感值分别为16.1 nH、13.4 nH和6.8 nH,变化率为136.7%。     

人工智能可控性探究

由当前人工智能的某些不可控案例出发,联想到人工智能的脆弱性与不确定性,试探性地提出了提高人工智能可控性的方法。主要分为五个部分：第一部分首先简述人工智能的提出与发展;第二部分介绍聊天机器人以及其他人工智能产品的具体不可控案例;第三部分主要探讨人工智能技术脆弱性和不确定性的来源;第四部分提出提高人工智能可控性的几点建议,包括增强人工智能的场景感知能力、在系统架构中部署可控节点以及通过可控节点健全监管评估体系;第五部分描述人工智能技术在光电等领域的展望。     

The use of arch index to characterize arch height: a digital imageprocessing approach

Attempts to evaluate foot arch types from footprint parameters have yielded conflicting results in the past. This could be caused by the uncertainty inherent in the definition of some footprint parameters and the inaccuracy during the footprint acquisition and the parameter calculation phases of the traditional methods. In order to avoid these problems, digital image processing methods were used to acquire and to calculate the Arch Index (AI), a parameter which is robust in its definition. A significant correlation (r=-0.70, p<0.0001) was found between AI and arch height. Therefore this study confirms that foot arch type does correlate with the footprint parameter, AI. This was further revealed by a new parameter, the modified arch index (MAI), which incorporates foot pressure information in the evaluation. MAI not only correlated well with arch height (r=-0.71, p<0.0001) but appeared to characterize abnormal foot types better than AI     

Design and application of a CMOS active inductor at Ku band based on a multi-objective optimizer

This paper presents a new design of a grounded active inductor (AI) with an improved topology based on Manetakis regulated cascode active inductor comprising of three control voltages for tunability. An additional pMOST was introduced in the design as a drain load at the output of nMOST source follower. The aim of this work is to design a CMOS AI at Ku band using AIDA-C, a state-of-the-art multi-objective multi-constraint circuit-level optimization tool. Firstly, a reasonable AI operating at Ku band was manually designed using a 130 nm technology. This circuit and its design variables were fed to AIDA-C as an element of the initial population. Then the sizing of the proposed AI MOSTs was optimized. AIDA-C circuit sizing tool is able to achieve not only one but a set of solutions for the AI exhibiting high quality factor at a predefined Ku band operating frequency. This set of alternative Pareto optimal solutions enables the designer to choose the most suitable circuit sizing for a given application. AI’s main performance parameters in terms of s parameters (s₁₁), quality factor (Q), inductance value (L), linearity, noise figure, power consumption and tunability based on control and biasing voltages are presented. Layout of the optimized AI is also presented. This AI was used to design active filters. Their selectivity, insertion losses and noise analysis is presented and discussed.