Search results

The purpose of this paper is to present a case‐based system for offline automatic programming in robotic assembly production. This system can reuse past learned robot programs to generate programs for new assembly tasks.

The approach used in this paper is case‐based reasoning. The assembly knowledge acquired from the robot program for an assembly task is retained in a case, which is composed of the primitive task description and the corresponding robot program schema. The retained cases are retrieved by matching features of their primitive task descriptions, and are reused to automatically program for new tasks by instantiating their robot program schemata.

A case not only can be reused as a whole, but also can be reused partly by synthesizing different parts of several cases to generate a program for a new task in a variant environment.

The teaching time of robots can be greatly reduced. This helps to introduce robots into small and medium enterprises.

This paper proposes a novel system that can automatically program for assembly tasks in various environments by flexibly reusing past robot programs.

The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

Following earlier claims that quantum‐inspired evolutionary algorithm (QIEA) may offer advantages in high‐dimensional environments, the purpose of this paper is to test a real‐valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability within both static and dynamic environments.

This paper compares the performance of both the QIEA and the canonical genetic algorithm (GA) on a series of test benchmark functions.

The results show that the QIEA obtains highly competitive results when benchmarked against the GA within static environments, while substantially outperforming both binary and real‐valued representation of the GA in terms of running time. Within dynamic environments, the QIEA outperforms GA in terms of stability and run time.

This paper suggests that QIEA has utility for real‐world high‐dimensional problems, particularly within dynamic environments, such as that found in real‐time financial trading.

The purpose of this paper is to provide an analysis on using two non‐conventional nonlinear estimating filters compared to the traditional linearized extended Kalman filter (EKF). This analysis will look at two state‐of‐the‐art applications and will provide insight to the problems associated with these applications.

The approach taken was to simulate both applications with three different filter designs: EKF, unscented Kalman filter, and particle filter. After results and explanations are given for both applications, then there is a comparison of results between the two applications to compare and contrast their findings.

This research shows how critical it is when selecting a filter for different applications. Not only is tuning the filter properly a necessity, but choosing a filter that is optimum for the application also greatly affects the accuracy and precision of the results.

As these filter methods are proven feasible for these applications, testing can move beyond simulation. Further research could compare other nonlinear filters to these results to determine if a better estimation technique exists.

This paper shows a lot of the issues one must face when choosing an estimation technique for their application as well as the impact the technique can have on the outcome.

This paper clearly describes the decision‐making criteria in regards to these two specific applications. These two applications are current technological problems that many are trying to solve. This paper shows where and why errors in calculations occur. It also offers insight into different ways to solve these problems when the specific application is taken into account.

This paper presents recently published resources on library instruction and information literacy. It provides an introductory overview and a selected annotated bibliography of publications covering various library types, study populations and research contexts. The selected bibliography is useful to efficiently keep up with trends in library instruction for busy practitioners, library science students and those wishing to learn about information literacy in other contexts.

This article annotates 424 English-language periodical articles, monographs, dissertations, theses and reports on library instruction and information literacy published in 2021. The sources were selected from the EBSCO platform for Library, Information Science, and Technology Abstracts (LISTA), Education Resources Information Center (ERIC), Scopus, ProQuest Dissertations and Theses, and WorldCat, published in 2021 that included the terms “information literacy,” “library instruction,” or “information fluency” in the title, abstract or keywords. The sources were organized in Zotero. Annotations summarize the source, focusing on the findings or implications. Each source was categorized into one of seven pre-determined categories: K-12 Education, Children and Adolescents; Academic and Professional Programs; Everyday Life, Community, and the Workplace; Libraries and Health Information Literacy; Multiple Library Types; and Other Information Literacy Research and Theory.

The paper provides a brief description of 424 sources and highlights sources that contain unique or significant scholarly contributions.

The information may be used by librarians, researchers and anyone interested as a quick and comprehensive reference to literature on library instruction and information literacy within 2021.

This conceptual paper explores the implications for the sociology of the professions of robo-advice (RA) provided by robo-advisors (RAs) as an early example of successfully programmed algorithmic knowledge managed by artificial intelligence (AI).

The authors examine the drivers of RAs, their success, characteristics, and establish RA as an early precursor of commercialized, programmed professional advice with implications for developments in the sociology of the professions.

Within the lens of the sociology of the professions, the success of RAs suggests that the diffusion of this innovation depends on three factors: the programmed flows of automated professional knowledge are minimally disruptive, they are less costly, and attract attention because of the “on-trend” nature of algorithmic authority guided by AI. The on-trend nature of algorithmic governance and its increasing public acceptance points toward an algorithmic paradox. The contradictions arise in the gap between RA marketed to the public and as a set of professional practices.

The incursion of RA-like disembodied advice into other professions is predicted given the emergence of tech-savvy clients, the tie between RA and updatable flows of big data, and an increasing shift to the “maker” or “do-it-yourself” movements.

Using the success of RAs in the financial industry, the authors predict that an AI-managed platform, despite the algorithmic paradox, is an avenue for growth with implications for researchers in the sociology of the professions.

As algorithms permeate nearly every aspect of digital life, artificial intelligence (AI) systems exert a growing influence on human behavior in the digital milieu. Despite its popularity, little is known about the roles and effects of algorithmic literacy (AL) on user acceptance. The purpose of this study is to contextualize AL in the AI environment by empirically examining the role of AL in developing users' information processing in algorithms. The authors analyze how users engage with over-the-top (OTT) platforms, what awareness the user has of the algorithmic platform and how awareness of AL may impact their interaction with these systems.

This study employed multiple-group equivalence methods to compare two group invariance and the hypotheses concerning differences in the effects of AL. The method examined how AL helps users to envisage, understand and work with algorithms, depending on their understanding of the control of the information flow embedded within them.

Our findings clarify what functions AL plays in the adoption of OTT platforms and how users experience algorithms, particularly in contexts where AI is used in OTT algorithms to provide personalized recommendations. The results point to the heuristic functions of AL in connection with its ties in trust and ensuing attitude and behavior. Heuristic processes using AL strongly affect the credibility of recommendations and the way users understand the accuracy and personalization of results. The authors argue that critical assessment of AL must be understood not just about how it is used to evaluate the trust of service, but also regarding how it is performatively related in the modeling of algorithmic personalization.

The relation of AL and trust in an algorithm lends strategic direction in developing user-centered algorithms in OTT contexts. As the AI industry has faced decreasing credibility, the role of user trust will surely give insights on credibility and trust in algorithms. To better understand how to cultivate a sense of literacy regarding algorithm consumption, the AI industry could provide examples of what positive engagement with algorithm platforms looks like.

User cognitive processes of AL provide conceptual frameworks for algorithm services and a practical guideline for the design of OTT services. Framing the cognitive process of AL in reference to trust has made relevant contributions to the ongoing debate surrounding algorithms and literacy. While the topic of AL is widely recognized, empirical evidence on the effects of AL is relatively rare, particularly from the user's behavioral perspective. No formal theoretical model of algorithmic decision-making based on the dual processing model has been researched.

This conceptual paper draws together an interdisciplinary approach to robo-advisors (RAs) as an example of an early and successful example of automated, programmed professional services.

Little is known about the forces driving this change in the delivery of professional service. This work explores the drivers of RAs, the degree of disruption incurred by the introduction of RAs, and how, as RAs advance, trust in algorithmic authority aids in legitimating RAs as smart information.

From the firms' perspective, the drivers include rebranding occasioned by the financial crisis (2008), the widening of the client base and the “on-trend” nature of algorithmic authority guided by artificial intelligence (AI) embedded in RAs. This examination of the drivers of RAs indicates that professional service automation is aligned with information society trends and is likely to expand.

Examining RAs as an indicator of the future introduction of programmed professional services suggests that success increases when the algorithmic authority in the programmed serves are minimally disruptive, trustworthy and expand the client base while keeping the knowledge domain of the profession under control of the industry.

Treating RAs as an early instance of successfully embedding knowledge in AI and algorithmically based platforms adds to the early stages of theory and practice in the monetization and automation of professional knowledge-based services.

This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy.

Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations.

Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.

While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.