Abstract: There is a gap in existing critical scholarship that engages with the ways in which current “machine listening” or voice analytics/biometric systems intersect with the technical specificities of machine learning. This article examines the sociotechnical assemblage of machine learning techniques, practices, and cultures that underlie these technologies. After engaging with various practitioners working in companies that develop machine listening systems, ranging from CEOs, machine learning engineers, data scientists, and business analysts, among others, I bring attention to the centrality of “learnability” as a malleable conceptual framework that bends according to various “ground-truthing” practices in formalizing certain listening-based prediction tasks for machine learning. In response, I introduce a process I call Ground Truth Tracings to examine the various ontological translations that occur in training a machine to “learn to listen.” Ultimately, by further examining this notion of learnability through the aperture of power, I take insights acquired through my fieldwork in the machine listening industry and propose a strategically reductive heuristic through which the epistemological and ethical soundness of machine learning, writ large, can be contemplated.

Abstract: Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-shot command customization with minimal user effort. Our model exhibits high robustness to different lighting, posture, and gesture conditions on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947 is achievable only using one shot, and its performance can be further boosted by adaptively learning from more data. This generalizability allowed us to develop a mobile silent speech interface empowered with on-device fine-tuning and visual keyword spotting. A user study demonstrated that with LipLearner, users could define their own commands with high reliability guaranteed by an online incremental learning scheme. Subjective feedback indicated that our system provides essential functionalities for customizable silent speech interactions with high usability and learnability.

Abstract: Concept-based interpretability methods aim to explain a deep neural network model's components and predictions using a pre-defined set of semantic concepts. These methods evaluate a trained model on a new, “probe” dataset and correlate the model's outputs with concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated in the literature. In this work, we identify and analyze three commonly overlooked factors in concept-based explanations. First, we find that the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets lead to very different explanations, suggesting that the generated explanations are not generalizable outside the probe dataset. Second, we find that concepts in the probe dataset are often harder to learn than the target classes they are used to explain, calling into question the correctness of the explanations. We argue that only easily learnable concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. We discuss the implications of our findings and provide suggestions for future development of concept-based interpretability methods. Code for our analysis and user interface can be found at https://github.com/princetonvisualai/OverlookedFactors

Abstract: In this era, insurance is an important instrument in processing financial plans considering that financial risks can occur regardless of place and time. However, in its implementation, insurance does not attract the attention and interest of the Indonesian people. Seeing this phenomenon, using the User Centered Design (UCD) method, this study aims to create a prototype of insurance features on the Bank Jago mobile application. This application implements usability principles such as learnability, memorability and satisfaction in introducing the importance of insurance for the community. Based on the method that has been used, this research produces a prototype of Bank Jago's insurance application that implements the usability principle. In this study, a prototype was produced that received a positive response from the community in terms of 65% learnability, 50% memorability, 70% satisfaction and 55% efficiency.

Abstract: The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advantage proposals and a variety of quantum machine learning algorithms. In this work, we provide an extensive characterization of the learnability of the output distributions of local quantum circuits. Our first result yields insight into the relationship between the efficient learnability and the efficient simulatability of these distributions. Specifically, we prove that the density modelling problem associated with Clifford circuits can be efficiently solved, while for depth $d=n^{\Omega(1)}$ circuits the injection of a single $T$-gate into the circuit renders this problem hard. This result shows that efficient simulatability does not imply efficient learnability. Our second set of results provides insight into the potential and limitations of quantum generative modelling algorithms. We first show that the generative modelling problem associated with depth $d=n^{\Omega(1)}$ local quantum circuits is hard for any learning algorithm, classical or quantum. As a consequence, one cannot use a quantum algorithm to gain a practical advantage for this task. We then show that, for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=\omega(\log(n))$ Clifford circuits is hard. This result places limitations on the applicability of near-term hybrid quantum-classical generative modelling algorithms.

Abstract: Structured editing can show benefits in learnability, tool building, and editing efficiency in programming. However, creating a usable structured editor is laborious and demanding, typically requiring tool builders to manually create or adjust editing interactions. We present Sandblocks, a system that allows users to automatically generate structured editors for every language with a formal grammar available. Our system’s input reconciliation process acts on arbitrary syntax trees to provides consistent interactions across our generated editors. Our editors’ editing experience is designed to be familiar to users from textual editing but, compared to previous work, requires no manual annotation in the grammars. We demonstrate our editors’ usability across languages through a user study (N=18). Compared to conventional text editors, even with minimal training, participants only took on average 21% (JS), 34% (Clojure), and 95% (RegExp) longer and reported that editing felt natural with a score of 6/7.

Abstract: The goal of theoretical inquiry is explanation: Why this, and not that? In the study of language, search for explanatory theory proceeds at two levels: for individual languages (a generative grammar in the broad sense) and for the general faculty of language fl (ug), the latter apparently a true species property, common to humans and without significant analogue in the animal world. ug must meet several conditions: learnability, evolvability, coverage. These conditions appear to conflict, and are far more severe than had earlier been supposed. A solution to the conundrum would be satisfaction of smt for ug combined with recourse to language-independent principles of computational efficiency, with diversity sequestered in components of language subject to simple algorithmic search. For the first time, hopes for such an outcome seem to be on the horizon, with significant implications if the hopes can be realized. I will outline some current work on these topics.

Abstract: That computers should be easy to learn and use is a rarely-questioned tenet of user interface design. But what do we gain from prioritising usability and learnability, and what do we lose? I explore how simplicity is not an inevitable truth of user interface design, but rather contingent on a series of events in the evolution of software. Not only does a rigid adherence to this doctrine place an artificial ceiling on the power and flexibility of software, but it is also culturally relative, privileging certain information cultures over others. I propose that for feature-rich software, negotiated complexity is a better target than simplicity, and we must revisit the ill-regarded relationship between learning, documentation, and software.

Abstract: Argumentation Maps support participants in geographically referenced debates as they occur, for example, as part of urban planning processes. In a quasi-naturalistic case study, 11 student participants discussed planning issues on the University of Toronto downtown campus. The analysis of this case study focuses on general usability aspects of an Argumentation Map prototype, such as cost of entry, efficiency, interactivity, and connectivity. By applying usability analysis methods from the field of human-computer interaction, we evaluate the learnability, memorability, and user satisfaction with this tool’s functionality. Our findings indicate that the participants were generally satisfied, but we include specific suggestions for improving the functionality of Argumentation Maps, e.g., with respect to map navigation, display of discussion contributions, and online status of participants. On a more general level, this case study contributes to the methods spectrum of research into participatory spatial decision support systems as an example of user testing in a realistic decision-making context.

Abstract: This article delves into the crucial issue of effectively implementing usability principles in educational internet resources. By engaging with the latest research in the field, we investigate the influential factors shaping the outcomes of online education. Through a comprehensive analysis, we identify and examine six well-established criteria of usability design: Information Quality, System Navigation, System Learnability, Visual Design, Instructional Assessment, and System Interactivity. Additionally, we propose the existence of a seventh criterion termed Responsiveness.To shed light on the practical application of usability principles, we focus on the open platform "Higher School Mathematics Teacher" as a case study. Through a survey administered to 203 respondents, comprising both teachers and students, we sought to gather their valuable perspectives as the initial users of the platform. The insights gained from this study provide guidance for the implementation of usability criteria on the platform, particularly during the development of online courses.The findings strongly suggest that all seven subcategories of usability are pivotal in the design of online courses on the platform. This research contributes to the ongoing discourse on usability implementation in educational technology, offering valuable insights for developers, educators, and researchers alike. By recognizing the significance of these criteria, educational internet resources can be enhanced to create more engaging, accessible, and effective learning environments.

Abstract: Abstract Full-fledged grammatical article systems as attested in Germanic and Romance languages are rather uncommon from a typological perspective. The frequency with which articles occur in these languages, together with the difficulty encountered in detecting them and the lack of a water-tight account of article use, make article errors one of the most frequent errors in language produced by L2 learners whose L1 does not feature an article system of similar complexity, all the while appearing unproblematic for L1 users. We present a conceptually and methodologically interdisciplinary approach to the grammatical category of articles in English and combine a usage-based, cognitive linguistic account of the function and use of articles that respects its discourse-based nature with a computational exploration of the challenges the system poses from the perspective of learning. Running a statistical classifier on a large sample of spoken and written discourse chunks extracted from the BNC and annotated for the five main determinants of article use reveals that Hearer Knowledge is the driver of a hierarchical system. Once Hearer Knowledge is acknowledged as the motivating principle of the category, article use becomes eminently predictable and restrictions are in line with the forms from which the articles have developed historically, with the and a acting as category defaults and zero acting as default override. Simulations with a computational model anchored in the psychology of learning shed light on whether and how human cognition would handle the proposed relations detected in the data. We find that different articles have different learnability profiles that, again, are in line with their historical development: while the can be learned from one strong indicator, the relationships for the zero article are less exclusive. On the basis of these findings, we argue that the article category appears as a referent tracking system that grammaticalizes the principles of “audience design”: it forces a speaker to track and mark reference from the vantage point of the memory of the hearer, thereby reducing the processing effort required from the hearer. This particular mindset inverses the typologically dominant situation in which this information is not explicitly marked by the speaker but implicitly retrieved from context by the hearer.

Abstract: Accurate and user-friendly rapid point-of-care diagnostic tests (POCT) are needed to optimize treatment of Neisseria gonorrhoeae, especially in low-resource settings where syndromic management is the standard of care for sexually transmitted infections. This study aimed to assess the acceptability and usability of a novel lateral flow assay and portable reader for the point-of-care detection of N. gonorrhoeae infection (NG-LFA). This mixed-methods study was conducted as part of a diagnostic performance and usability evaluation of a prototype NG-LFA for detection of N. gonorrhoeae in symptomatic men and women at primary healthcare facilities in the Buffalo City Metro, South Africa. The Standardized System Usability Scale (SUS) was administered, and in-depth interviews were conducted among healthcare professionals (HCPs) and fieldworkers (FWs) at pre-implementation, initial use and 3- and 6-month study implementation to assess user expectations, practical experience, and future implementation considerations for the NG-LFA. Data collection and analysis was guided by the Health Technology Adoption Framework, including new health technology attributes, learnability, satisfaction, and suitability. The framework was adapted to include perceived durability. A total of 21 HCPs and FWs were trained on the NG-LFA use. SUS scores showed good to excellent acceptability ranging from 78.8–90.6 mean scores between HCPs and FWs across study time points. All transcripts were coded using Dedoose and qualitative findings were organized by learnability, satisfaction, suitability, and durability domains. Usability themes are described for each time point. Initial insecurity dissipated and specimen processing dexterity with novel POCT technology was perfected over time especially amongst FWs through practical learning and easy-to-use instructions (learnability). Participants experienced both positive and negative test results, yielding perceived accuracy and minimal testing challenges overall (satisfaction). By 3- and 6-month use, both HCPs and FWs found the NG-LFA convenient to use in primary health care facilities often faced with space constraints and outlined perceived benefits for patients (suitability and durability). Findings show that the NG-LFA device is acceptable and usable even amongst paraprofessionals. High SUS scores and qualitative findings demonstrate high learnability, ease-of-use and suitability that provide valuable information for first-step scale-up requirements at primary healthcare level. Minor prototype adjustments would enhance robustness and durability aspects.

Abstract: Usage-based theories of language acquisition have extensively documented the processes by which children acquire language through communicative interaction. Notably, Tomasello (2003) distinguishes two main cognitive capacities that underlie human language acquisition: intention reading and pattern finding. Intention reading is the process by which children try to continuously reconstruct the intended meaning of their interlocutors. Pattern finding refers to the process that allows them to distil linguistic schemata from multiple communicative interactions. Even though the fields of cognitive science and psycholinguistics have studied these processes in depth, no faithful computational operationalisations of these mechanisms through which children learn language exist to date. The research on which we report in this paper aims to fill part of this void by introducing a computational operationalisation of syntactico-semantic pattern finding. Concretely, we present a methodology for learning grammars based on similarities and differences in the form and meaning of linguistic observations alone. Our methodology is able to learn compositional lexical and item-based constructions of variable extent and degree of abstraction, along with a network of emergent syntactic categories. We evaluate our methodology on the CLEVR benchmark dataset and show that the methodology allows for fast, incremental and effective learning. The constructions and categorial network that result from the learning process are fully transparent and bidirectional, facilitating both language comprehension and production. Theoretically, our model provides computational evidence for the learnability of usage-based constructionist theories of language acquisition. Practically, the techniques that we present facilitate the learning of computationally tractable, usage-based construction grammars, which are applicable for natural language understanding and production tasks.

Abstract: In 2030, it is expected that 15% of the country's population will be classified as elderly and there is driving up demand for elderly healthcare services. The evolution of digital health technology has emerged as a solution to this issue. However, there has been a recent decline in the elderly adoption of digital health technologies. This issue is worsened by the emergence of interfaces and interaction styles in newly developed technologies. A systematic review was conducted in this article to investigate the usability measures used to improve the user experience of digital health technology among the elderly. This study includes articles selected from the Web of Science and Scopus databases, both of which are well-established. Using thematic analysis, data from 29 articles were analyzed, yielding four main themes: i) effectiveness; i) efficiency; iii) satisfaction; and iv) learnability. The four main themes generated 12 sub-themes. The appearance, functionality, and structure of new digital health technology are the primary barriers to adoption. User interface (UI) design should take into account the limitations of elderly users. Additionally, elderly users require motivation, support, and training to utilize digital health technologies effectively. This study's findings make significant contributions to digital health and gerontechnology fields.

Abstract: Financial literacy is a person's skills regarding financial knowledge and behavior. In 2019, the National Survey of Financial Literacy and Inclusion (SNLIK) stated that Indonesia had a low level of financial literacy with a percentage of 38.03%. In 2016, the Indonesian government incorporated financial literacy into the K13 curriculum for the high school level to improve financial literacy at the student level. In line with that, students need facilities to help them learn financial literacy. This study aims to build an interaction design of financial mathematics calculators using the User-Centered Design method. The limitation of this study is the design uses a mobile web platform that targets high school students. The results of usability testing on effectiveness get an average task-completion rate score of 92.3%, an average efficiency of 92.0% overall relative efficiency, user satisfaction using the System Usability Scale (SUS) gets a score of 93.2 for the usability factor and a score of 74 for the learnability factor. The findings of this study indicate that the application is easy to use and provides a positive experience in learning financial literacy. The current study's results will support this application's use in future research as a new tool for providing financial literacy.

Abstract: Online learning of revenue-optimal auctions is a fundamental problem in mechanism design without priors. Nevertheless, all the existing positive results assume that the auctioneer optimizes over a parameterized class of auctions, such as pricings and auctions with reserves. This is perhaps not surprising given that natural correlations that occur in online sequences pose a challenge to characterizing a succinct class of revenue-optimal auctions. This has left behind a significant gap in our understanding of online-learnability of general classes of non-parametric auctions. We provide the first positive results for online learnability of a non-parametric auction class, for smooth adversaries and the class of smooth auctions. In a nutshell, an online adversary is smooth (in the style of Smoothed analysis [Spielman and Teng, 2004] in online learning [Haghtalab et al., 2021]) if the bid distribution has bounded density at every time step, and an auction is smooth if the level sets of its revenue function have small boundaries. We prove the following fundamental guarantees: (1) Revenue maximization in the class of smooth auctions is online-learnable, against smooth adversaries. (2) It is impossible to construct a no-regret algorithm even for the class of smooth auctions against worst-case adversaries. (3) It is impossible to construct a no-regret algorithm for the class of all incentive-compatible auctions even against smooth adversaries. This gives a strong characterization of when and which class of non-parametric auctions are online-learnable. To illustrate the generality of the class of smooth auctions we show that it contains the class of all monotone-revenue auctions, as well as, the class of all competition-monotone auctions. This brings up an interesting observation: while independence across bids leads to the optimal auctions being monotone, significantly weaker assumptions, compared to monotonicity of revenue, are sufficient for learnability.

Abstract: In-context learning is a surprising and important phenomenon that emerged when modern language models were scaled to billions of learned parameters. Without modifying a large language model's weights, it can be tuned to perform various downstream natural language tasks simply by including concatenated training examples of these tasks in its input. Though disruptive for many practical applications of large language models, this emergent learning paradigm is not well understood from a theoretical perspective. In this paper, we propose a first-of-its-kind PAC based framework for in-context learnability, and use it to provide the first finite sample complexity results for the in-context learning setup. Our framework includes an initial pretraining phase, which fits a function to the pretraining distribution, and then a second in-context learning phase, which keeps this function constant and concatenates training examples of the downstream task in its input. We use our framework in order to prove that, under mild assumptions, when the pretraining distribution is a mixture of latent tasks (a model often considered for natural language pretraining), these tasks can be efficiently learned via in-context learning, even though the model's weights are unchanged and the input significantly diverges from the pretraining distribution. Our theoretical analysis reveals that in this setting, in-context learning is more about identifying the task than about learning it, a result which is in line with a series of recent empirical findings. We hope that the in-context learnability framework presented in this paper will facilitate future progress towards a deeper understanding of this important new learning paradigm.

Abstract: Combinatorial optimisation problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor in practice. The models typically consist of hard and soft constraints, or combine hard constraints with an objective function. We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. These positive and negative examples show – in a particular context – whether the solutions are good enough or not. We develop our framework using the MAX-SAT formalism as it is a simple yet powerful setting having these features. We study the learnability of MAX-SAT models. Our theoretical results show that high-quality MAX-SAT models can be learned from contextual examples in the realisable and agnostic settings, as long as the data satisfies an intuitive “representativeness” condition. We also contribute two implementations based on our theoretical results: one leverages ideas from syntax-guided synthesis while the other makes use of stochastic local search techniques. The two implementations are evaluated by recovering synthetic and benchmark models from contextual examples. The experimental results support our theoretical analysis, showing that MAX-SAT models can be learned from contextual examples. Among the two implementations, the stochastic local search learner scales much better than the syntax-guided implementation while providing comparable or better models.

Abstract: Abstract The foraging behavior of animals is a paradigm of target search in nature. Understanding which foraging strategies are optimal and how animals learn them are central challenges in modeling animal foraging. While the question of optimality has wide-ranging implications across fields such as economy, physics, and ecology, the question of learnability is a topic of ongoing debate in evolutionary biology. Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning framework. To this end, we model foragers as learning agents. We first prove theoretically that maximizing rewards in our reinforcement learning model is equivalent to optimizing foraging efficiency. We then show with numerical experiments that, in the paradigmatic model of non-destructive search, our agents learn foraging strategies which outperform the efficiency of some of the best known strategies such as Lévy walks. These findings highlight the potential of reinforcement learning as a versatile framework not only for optimizing search strategies but also to model the learning process, thus shedding light on the role of learning in natural optimization processes.

Abstract: Most research into causal learning has focused on atemporal contingency data settings while fewer studies have examined learning and reasoning about systems exhibiting events that unfold in continuous time. Of these, none have yet explored learning about preventative causal influences. How do people use temporal information to infer which components of a causal system are generating or preventing activity of other components? In what ways do generative and preventative causes interact in shaping the behavior of causal mechanisms and their learnability? We explore human causal structure learning within a space of hypotheses that combine generative and preventative causal relationships. Participants observe the behavior of causal devices as they are perturbed by fixed interventions and subject to either regular or irregular spontaneous activations. We find that participants are capable learners in this setting, successfully identifying the large majority of generative, preventative and non-causal relationships but making certain attribution errors. We lay out a computational-level framework for normative inference in this setting and propose a family of more cognitively plausible algorithmic approximations. We find that participants' judgment patterns can be both qualitatively and quantitatively captured by a model that approximates normative inference via a simulation and summary statistics scheme based on structurally local computation using temporally local evidence.

Abstract: Currently, over 4000 bariatric procedures are performed annually in Switzerland. To improve outcomes, patients need to have good knowledge regarding postoperative nutrition. To potentially provide them with knowledge between dietetic consultations, a health bot (HB) was created. The HB can answer bariatric nutrition questions in writing based on artificial intelligence.This study aims to evaluate the usability and perception of the HB among patients receiving bariatric care.Patients before or after bariatric surgery tested the HB. A mixed methods approach was used, which consisted of a questionnaire and qualitative interviews before and after testing the HB. The dimensions usability of, usefulness of, satisfaction with, and ease of use of the HB, among others, were measured. Data were analyzed using R Studio (R Studio Inc) and Excel (Microsoft Corp). The interviews were transcribed and a summary inductive content analysis was performed.A total of 12 patients (female: n=8, 67%; male: n=4, 33%) were included. The results showed excellent usability with a mean usability score of 87 (SD 12.5; range 57.5-100) out of 100. Other dimensions of acceptability included usefulness (mean 5.28, SD 2.02 out of 7), satisfaction (mean 5.75, SD 1.68 out of 7), and learnability (mean 6.26, SD 1.5 out of 7). The concept of the HB and availability of reliable nutrition information were perceived as desirable (mean 5.5, SD 1.64 out of 7). Weaknesses were identified in the response accuracy, limited knowledge, and design of the HB.The HB's ease of use and usability were evaluated to be positive; response accuracy, topic selection, and design should be optimized in a next step. The perceptions of nutrition professionals and the impact on patient care and the nutrition knowledge of participants need to be examined in further studies.

Abstract: Question answering (QA) models for reading comprehension tend to exploit spurious correlations in training sets and thus learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut solutions can achieve human-level performance in shortcut examples where shortcuts are valid, but these same behaviors degrade generalization potential on anti-shortcut examples where shortcuts are invalid. Various methods have been proposed to mitigate this problem, but they do not fully take the characteristics of shortcuts themselves into account. We assume that the learnability of shortcuts, i.e., how easy it is to learn a shortcut, is useful to mitigate the problem. Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. We also find that the availability of the preferred shortcuts tends to make the task easier to perform from an information-theoretic viewpoint. Lastly, we experimentally show that the learnability of shortcuts can be utilized to construct an effective QA training set; the more learnable a shortcut is, the smaller the proportion of anti-shortcut examples required to achieve comparable performance on shortcut and anti-shortcut examples. We claim that the learnability of shortcuts should be considered when designing mitigation methods.

Abstract: This study investigated whether speaker variability in phonetic training benefits vowel learnability by Arabic learners of English. Perception training using High-Variability stimuli in laboratory studies has been shown to improve both the perception and production of Second Language sounds in adults and children and has become the dominant methodology for investigating issues in Second Language acquisition. Less consideration is given to production training, in which Second Language learners focus on the role of the articulators in producing second language sounds. This study aimed to assess the role of speaker variability by comparing the effect of using High-Variability and Low-Variability stimuli for production training in a classroom setting. Forty-six Arabic children aged 9-12 years were trained on 18 Standard Southern British English vowels in five training sessions over two weeks and were tested before and after training on their vowel production and category discrimination. The results indicate that Low-Variability stimuli may be more beneficial for children, however, High-Variability stimuli may alter some phonetic cues. Furthermore, the results suggest that production training may be used to improve the perception and production of Second Language sounds, but also to inform the design of Second Language pronunciation learning programmes and theories of Second Language acquisition.

Abstract: We study the learnability of symbolic finite state automata (SFA), a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. The main focus of our work lies in the learnability of SFAs under the paradigm of identification in the limit using polynomial time and data, and its strengthening efficient identifiability, which are concerned with the existence of a systematic set of characteristic samples from which a learner can correctly infer the target language. We provide a necessary condition for identification of SFAs in the limit using polynomial time and data, and a sufficient condition for efficient learnability of SFAs. From these conditions we derive a positive and a negative result. The performance of a learning algorithm is typically bounded as a function of the size of the representation of the target language. Since SFAs, in general, do not have a canonical form, and there are trade-offs between the complexity of the predicates on the transitions and the number of transitions, we start by defining size measures for SFAs. We revisit the complexity of procedures on SFAs and analyze them according to these measures, paying attention to the special forms of SFAs: normalized SFAs and neat SFAs, as well as to SFAs over a monotonic effective Boolean algebra. This is an extended version of the paper with the same title published in CSL'22.

Abstract: Abstract We study a variant of the problem of synthesizing Mealy machines that enforce LTL specifications against all possible behaviours of the environment, including hostile ones. In the variant studied here, the user provides the high level LTL specification $$\varphi $$ φ of the system to design, and a set E of examples of executions that the solution must produce. Our synthesis algorithm first generalizes the user-provided examples in E using tailored extensions of automata learning algorithms, while preserving realizability of $$\varphi $$ φ . Second, it turns the (usually) incomplete Mealy machine obtained by the learning phase into a complete Mealy machine realizing $$\varphi $$ φ . The examples are used to guide the synthesis procedure. We prove learnability guarantees of our algorithm and prove that our problem, while generalizing the classical LTL synthesis problem, matches its worst-case complexity. The additional cost of learning from E is even polynomial in the size of E and in the size of a symbolic representation of solutions that realize $$\varphi $$ φ , computed by the synthesis tool Acacia-Bonzai . We illustrate the practical interest of our approach on a set of examples.

Abstract: The study aims to develop a mobile application catering to motorists across various locations in the Philippines. This app aims to provide motorists with high-quality vehicle services, including mechanical repairs to maintain their cars in good condition and other services focused on ensuring vehicle safety. The researchers also offer assistance in utilizing the mobile application, acting as a service that connects users with skilled mechanics to fulfill their automotive needs. To access these services, businesses, and consumers would need to create an account on the open platform provided by the mobile app. The development of this app holds the potential to expand the range of assistance offered by the business and bring benefits to the company. In today’s world, where mobile and Internet usage is prevalent, services that utilize these technologies allow companies to gather information efficiently and deliver services that benefit both the business and the consumer. The assessment of the various attributes based on the PACMAD framework indicates that users have a highly positive perception of the application. The application scored well in efficiency, effectiveness, learnability, memorability, error resolution, satisfaction, and cognitive load. These findings highlight the application’s strengths and provide valuable insights for further improvements, ensuring an enhanced user experience and continued user satisfaction.

Abstract: This study aims to determine the benefits of the Mendeley application for students inwriting scientific papers for library and information science students using the ISO/IEC9126-2 Usability measurement method based on the four characteristics as indicators,namely Understandability, Learnability, Operability, and Attractiveness. This study used anexperimental method, namely pre-experimental designs. Pre-Experimental Designs Thisresearch is a design that includes pre-test, treatment and post-test. The population in thisstudy were library and information science students class of 2020. The number of samplesin the study were 47 people. The sampling technique is basic accidental examining(inadvertent inspecting), namely distributing questionnaires to respondents using Googlestructure via the WhatsApp application. The results of the pre-test showed that theunderstanding level of the Mendeley application reached 2.66%, the operability level of theMendeley application reached 2.15%, the learnability level of the Mendeley applicationreached 1.51 %, and the attractiveness of Mendeley applications is 3.11%. The results of thepost test showed that the understanding level of the Mendeley application reached 7.11%,the operability level of the Mendeley application reached 10.4%, the learnability level of theMendeley application reached 3.45 %, and the attractiveness of the Mendeley application is7.55%. From the pre test results, the post test results increase in the utilization andoperation of the Mendeley application. Based on these results it is concluded that theMendeley application has been utilized by users properly.

Abstract: Augmented Reality (AR) is a technology that can turn virtual objects in the form of two dimensions (2D) or three dimensions (3D) into an object that looks real, then able to display objects in real-time. Using AR technology, you can visualize learning material into 3D objects to make it easier to understand when using it as a learning medium. This research aims to develop an AR application with multiple marker features that can be used as an interactive learning medium. The method used is Research and Development (R&D), and the development model used is 4D. The evaluation used was the Software Usability Measurement Inventory (SUMI), involving 11th-grade which involved 30 students consisting of 20 male students and 10 female students, the parameters assessed in this evaluation were between others are Efficiency, Affect, Helpfulness, Control, and Learnability. Based on the analysis performed on these parameters, the results show that all five parameters obtain valid and reliable results for each parameter in the validity and reliability tests with a Cronbach's alpha score of 0.934 (Efficiency), 0.868 (Affect), 0.917 (Helpfulness), 0.878 (Control), and 0.919 (Learnability). Thus, this multiple marker-based interactive learning media, Augmented Reality (AR), can be used in learning activities.