Macroelectronics are flexible electronics that cover a large area. The most visible example of macroelectronics is flat-panel displays. Other emerging applications include rollable display, printable thin film solar cell and electronic skin. Flat-panel displays fabricated on glass substrates are fragile so fabricating directly on flexible substrates, such as polymers is being explored. Displays made on thin polymer substrates can be more rugged than glass. In September 2005, Philips Polymer Vision revealed the world's first prototype of a rollable electronic reader, which can unfold to a 5-inch display and roll back into a pocket-size (100×60×20 mm) device. Thin-film devices on flexible polymer substrates can lend themselves to low-cost fabrication processes (i.e., roll-to-roll printing), resulting in lightweight, rugged and flexible macroelectronic products.
Human–AI interaction
Human–AI interaction is a developing field of research and a sub-field of human–computer interaction (HCI). HCI is a field of research that explores the interactions between humans and computer-based technology, focusing on design implementation, user experience, and psychological factors. With the proliferation of artificial intelligence (AI), there has developed a sub-section of HCI research dedicated specifically to artificial intelligence and how people interact with and are impacted by it. This is human–AI interaction, abbreviated either as HAX or HAII. == Introduction == Artificial intelligence (AI), in general, has fluid definitions and varied research applications, but in brief can be applied to mechanizing tasks that would require human intelligence to complete. AI are tools designed to replicate the human abilities of navigating uncertainty, active learning, and processing information in different contexts. Within the context of HCI and HAX research, artificial intelligence can be broken into two sub-fields, natural language processing (NLP) and computer vision (CV). AI technologies notably include machine-learning, deep-learning and neural networks, and large-language models (LLMs). As a new and rapidly developing technology, AI is changing how computers work and therefore changing how humans interact with computers. Unlike the traditional human-computer interaction, where a human directs a machine, human-AI interaction is characterized by a more collaborative relationship between the computer program (the AI) and the human user, as AI is perceived as an active agent rather than a tool. This changing dynamic creates new questions and necessitates new research methods that are not present in traditional HCI research. According to a scoping review on the state of the discipline, the HAX field comprises research on the "design, development, and evaluation of AI systems" and encompasses the themes of human-AI collaboration, human-AI competition, human-AI conflict, and human-AI symbiosis. == Design == Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media. Ethical Guidelines (Framework for ethical AI development) == User Experience (UX) == This section should handle research on how users interact with tools. What techniques do they use, do they develop habits, what types of programs and devices are they using to access these tools, what do they use these tools to do exactly. === Cognitive Frameworks in AI Tool Users === AI has been viewed with various expectations, attributions, and often misconceptions. Many people exclusively understand AI as the LLM chatbots they interact with, like ChatGPT or Claude, or other generative AI programs. [Insert section: discuss how people interact with these specific AI tools as a connection to the following paragraphs] Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it; more interaction with AI builds a better mental model that a person may build to produce better prompt outcomes. Research on human-AI interaction has emphasized that users develop mental models of AI systems and revise those models through repeated use, feedback, and explanation, while design research has stressed the importance of communicating capabilities and limitations early and supporting trust calibration through explanation and correction. In a 2025 SSRN working paper, John DeVadoss proposed "Hypothetico-Deductive Interaction" (HDI), a framework that describes human-AI interaction as a mutual process of conjecture and refutation in which users test assumptions about an AI system's capabilities while the system infers and updates assumptions about user goals through its responses and clarifying questions. DeVadoss argued that this framing helps explain prompt iteration, weak capability awareness, and trust miscalibration, and suggested design responses such as clearer communication of uncertainty, easier correction, actionable explanations, and safer failure modes. == Research themes == === Human-AI collaboration === Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end. Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example: the quality and speed of customer service tasks increase when a human agent collaborates with AI, training on specific models allows AI to improve diagnoses in clinical settings, and AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively. Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working alone could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition. === Human-AI competition === Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030. Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it. There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice. However, people are not always able to tell apart tasks completed by AI or other humans. See AI takeover for more information. It is also notable that this sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians. == Research on the psychological impacts of AI == === Perception on others who use AI === As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks. AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI. Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI. === Bias, AI, and human === Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected as the female gendering of AI technologies which conceptualizes females as a helpful assistant. == Emotional connection with AI == Human-AI interaction has been theorized in the context of interpersonal relationships mainly in social psychology, communications and media studies, and as a technology interface through the lens of hu
Organizational metacognition
Organizational metacognition is knowing what an organization knows, a concept related to metacognition, organizational learning, the learning organization and sensemaking. It is used to describe how organizations and teams develop an awareness of their own thinking, learning how to learn, where awareness of ignorance can motivate learning. The organizational deutero-learning concept identified by Argyris and Schon defines when organizations learn how to carry out single-loop and double-loop learning. It has also been described as learning how to learn through a process of collaborative inquiry and reflection (evaluative inquiry). "When an organization engages in deutero-learning its members learn about the previous context for learning. They reflect on and inquire into previous episodes of organizational learning, or failure to learn. They discover what they did that facilitated or inhibited learning, they invent new strategies for learning, they produce these strategies, and they evaluate and generalize what they have produced" Learning what facilitates and inhibits learning enables organizations to develop new strategies to develop their knowledge. For example, identification of a gap between perceived performance (such as satisfaction) and actual performance (outcomes) creates an awareness that makes the organization understand that learning needs to occur, driving appropriate changes to the environment and processes. == Learning prototypes == Wijnhoven (2001) grouped four learning prototypes that best meet learning needs, the match between these needs and learning norms dictating an organization's learning capabilities; deutero-learning is the acquisition of these capabilities. knowledge gap analysis classification of problems to select operationally required knowledge and skills coping with organizational tremors and jolts by anticipation, response and adjustments of behavioural repertoires decisional uncertainty measurement == Terminological ambiguities == Organizational metacognition and organizational deutero-learning have both been described as the concept or phenomenon where organizations learn how to learn. Argyris and Schon (1978) place deutero-learning into their cognitive theory of action framework, neglecting aspects of adaptive behaviour and context core to Bateson's (1972) original definitions. In order to resolve terminological ambiguities, Visser (2007) reviewed and reformulated the concept of deutero-learning as, "the behavioral adaptation to patterns of conditioning in relationships in organizational contexts, distinguishing it from meta-learning and planned learning" (pg. 659). == Significance == Organizational metacognition is considered a key norm to the prescriptive concept of the learning organization. Its significance has been recognized by industry, the military and in disaster response. == Examples in practice == Examples of poor metacognition (deutero-learning) have been described in knowledge network environments, "Knowledge networking is important to most competitive enterprises today. Enterprise knowledge is becoming ever more specialized in nature, so no single person or organization can know everything in detail. Hence addressing complex, multidisciplinary problems requires developing and accessing a network of knowledgeable people and organizations. The problem is, many otherwise knowledgeable people and organizations are not fully aware of their knowledge networks, and even more problematic, they are not aware that they are not aware. This focuses our attention toward organizational metacognition."
Energy informatics
Energy informatics is a research field covering the use of information and communication technology to address energy utilization and management challenges. Methods used for "smart" implementations often combine IoT sensors with artificial intelligence and machine learning. Energy Informatics is founded on flow networks that are the major suppliers and consumers of energy. Their efficiency can be improved by collecting and analyzing information. == Application areas == The field among other consider application areas within: Smart Buildings by developing ICT-centred solutions for improving the energy-efficiency of buildings. Smart Cities by investigating the synergies between demand patterns and supply availability of energy flows in cities and communities to improve energy efficiency, increase integration of renewable sources, and provide resilience towards system faults caused by extreme situations, like hurricanes and flooding. Smart Industries including the development of ICT-centred solutions for improving the energy efficiency and predictability of energy intensive industrial processes, without compromising process and product quality. Smart Energy Networks by developing ICT-centred solutions for coordinating the supply and demand in environmentally sustainable energy networks.
Token-based replay
Token-based replay technique is a conformance checking algorithm that checks how well a process conforms with its model by replaying each trace on the model (in Petri net notation ). Using the four counters produced tokens, consumed tokens, missing tokens, and remaining tokens, it records the situations where a transition is forced to fire and the remaining tokens after the replay ends. Based on the count at each counter, we can compute the fitness value between the trace and the model. == The algorithm == Source: The token-replay technique uses four counters to keep track of a trace during the replaying: p: Produced tokens c: Consumed tokens m: Missing tokens (consumed while not there) r: Remaining tokens (produced but not consumed) Invariants: At any time: p + m ≥ c ≥ m {\displaystyle p+m\geq c\geq m} At the end: r = p + m − c {\displaystyle r=p+m-c} At the beginning, a token is produced for the source place (p = 1) and at the end, a token is consumed from the sink place (c' = c + 1). When the replay ends, the fitness value can be computed as follows: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})} == Example == Suppose there is a process model in Petri net notation as follows: === Example 1: Replay the trace (a, b, c, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity c {\displaystyle \mathbf {c} } consumes 1 token and produces 1 token ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} and c = 2 + 1 = 3 {\displaystyle c=2+1=3} ). Step 5: The activity d {\displaystyle \mathbf {d} } consumes 2 tokens and produces 1 token ( p = 5 + 1 = 6 {\displaystyle p=5+1=6} , c = 3 + 2 = 5 {\displaystyle c=3+2=5} ). Step 6: The token at the end place is consumed ( c = 5 + 1 = 6 {\displaystyle c=5+1=6} ). The trace is complete. The fitness of the trace ( a , b , c , d {\displaystyle \mathbf {a,b,c,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 0 6 ) + 1 2 ( 1 − 0 6 ) = 1 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {0}{6}})+{\frac {1}{2}}(1-{\frac {0}{6}})=1} === Example 2: Replay the trace (a, b, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity d {\displaystyle \mathbf {d} } needs to be fired but there are not enough tokens. One artificial token was produced and the missing token counter is increased by one ( m = 1 {\displaystyle m=1} ). The artificial token and the token at place [ b , d ] {\displaystyle [\mathbf {b,d} ]} are consumed ( c = 2 + 2 = 4 {\displaystyle c=2+2=4} ) and one token is produced at place end ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} ). Step 5: The token in the end place is consumed ( c = 4 + 1 = 5 {\displaystyle c=4+1=5} ). The trace is complete. There is one remaining token at place [ a , c ] {\displaystyle [\mathbf {a,c} ]} ( r = 1 {\displaystyle r=1} ). The fitness of the trace ( a , b , d {\displaystyle \mathbf {a,b,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 1 5 ) + 1 2 ( 1 − 1 5 ) = 0.8 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {1}{5}})+{\frac {1}{2}}(1-{\frac {1}{5}})=0.8}
International Conference on Language Resources and Evaluation
The International Conference on Language Resources and Evaluation is an international conference organised by the ELRA Language Resources Association every other year (on even years) with the support of institutions and organisations involved in Natural language processing. The series of LREC conferences was launched in Granada in 1998. == History of conferences == The survey of the LREC conferences over the period 1998-2013 was presented during the 2014 conference in Reykjavik as a closing session. It appears that the number of papers and signatures is increasing over time. The average number of authors per paper is higher as well. The percentage of new authors is between 68% and 78%. The distribution between male (65%) and female (35%) authors is stable over time. The most frequent technical term is "annotation", then comes "part-of-speech". == The LRE Map == The LRE Map was introduced at LREC 2010 and is now a regular feature of the LREC submission process for both the conference papers and the workshop papers. At the submission stage, the authors are asked to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors are then gathered in a global matrix called the LRE Map. This feature has been extended to several other conferences.
Harold Borko
Harold Borko (1922-2012) was an American psychologist and researcher working primarily in the field of information science. == Biography == Borko was born in 1922 in New York City, New York. After serving in the US Army from 1942 to 1946 he obtained a BA in Psychology from the University of California, Los Angeles in 1948 and both his MA and PhD from the University of Southern California in Psychology in 1952. He returned to the army as a psychologist until 1956 after which he began a career working in and teaching information science. He died in California in 2012. == Information Science Career == After leaving the military Borko began working at the RAND Corporation as a Systems Training Specialist in 1956 and moved to the Systems Development Corporation a year later working in the Language Processing and Retrieval department. Alongside this work he taught Psychology at USC from 1957-65 and then moved into teaching Library Science at UCLA from 1965. In 1967 Borko left his role at the Systems Development Corporation and continued as a full-time professor at UCLA until his retirement in 1993.. From 1961 to 1995 Borko authored and co-authored over 100 articles on new developments in the field as well as the historiography of information science. He served as an editor of the Journal of Educational Data Processing from 1963-1975 and as President of the American Society for Information Science in 1966 == Partial list of works == Borko, H. (1962, May). The construction of an empirically based mathematically derived classification system. In Proceedings of the May 1-3, 1962, spring joint computer conference (pp. 279-289). Borko, H., & Bernick, M. (1963). Automatic document classification. Journal of the ACM (JACM), 10(2), 151-162. Borko, H. (1964). The Storage and Retrieval of Educational Information. Journal of Teacher Education, 15(4), 449-452. Borko, H. (1964). Measuring the reliability of subject classification by men and machines. American Documentation, 15(4), 268-273. Borko, H. (1965). The conceptual foundations of information systems. Borko, H. (1968), Information science: What is it?†. Amer. Doc., 19: 3-5. https://doi.org/10.1002/asi.5090190103 Borko, H. (1970). Experiments in book indexing by computer. Information storage and retrieval, 6(1), 5-16. Borko, H. (1985). An introduction to computer-based library systems (Lucy A. Tedd). Education for Information, 3(1), 61.