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In this paper, we present a novel Artificial Intelligence (AI) -empowered system that enhances large language models and other machine learning tools with rules to provide primary care diagnostic advice to patients. Specifically, we introduce a novel methodology, represented through a process diagram, which allows the definition of generative AI processes and functions with a focus on the rule-augmented approach. Our methodology separates various components of the generative AI process as blocks that can be used to generate an implementation data flow diagram. Building upon this framework, we utilize the concept of a dialogue process as a theoretical foundation. This is specifically applied to the interactions between a user and an AI-empowered software program, which is called "Med|Primary AI assistant" (Alpha Version at the time of writing), and provides symptom analysis and medical advice in the form of suggested diagnostics. By leveraging current advancements in natural language processing, a novel approach is proposed to define a blueprint of domain-specific knowledge and a context for instantiated advice generation. Our approach not only encompasses the interaction domain, but it also delves into specific content that is relevant to the user, offering a tailored and effective AI–user interaction experience within a medical context. Lastly, using an evaluation process based on rules, defined by context and dialogue theory, we outline an algorithmic approach to measure content and responses. [ABSTRACT FROM AUTHOR]

In this paper, we present an Intelligent Tutoring System (ITS), for use in teaching the logic of computer programming and the programming language 'C'. The aim of the ITS is to adapt the delivered learning material and the lesson sequence to the knowledge level and learning needs of each individual student. The adaptation of the presented ITS is based on fuzzy logic and a machine learning technique. Particularly, the system uses the distance weighted k-nearest neighbor algorithm to detect the learner's knowledge level and abilities concerning computer programming during her/ his first interaction with the system. Next and during subsequent interactions of the learner with the system, fuzzy logic is used to identify the learner's current knowledge level and potential misconceptions. The system takes into consideration the knowledge dependencies that exist among the domain concepts of the learning material and, applying fuzzy rules, decides about the learning material that has to be delivered to the learner as well as the lesson sequence. The system has been fully implemented and evaluated through t-tests. The evaluation results show that the combination of machine learning (for initially identifying the student's learning abilities and needs) with fuzzy logic (for the continuous identification of the learner's current knowledge level and misconceptions) provides more personalized learning experience, promotes the active participation of students in the learning process and results in decrease in the number of dropouts. [ABSTRACT FROM AUTHOR]

A time domain algorithm for the computation of the maximum likelihood estimate of the location of a known scattering object from wide-band scattering data obtained in a suite of scattering experiments is proposed. It consists of a three-step procedure that includes data filtering, time-domain backpropagation and coherent summation. Results confirm that the log likelihood function for estimation of the location of a known scattering object from noisy wideband scattering data can be calculated through a time-domain, filtered backpropagation algorithm.

Thermophysical algebraic invariance formulation is a robust method for interpretation of invariant features of radiometrically uncalibrated infrared (IR) imagery for ATR and site change detection. The approach focuses on internal object composition and thermophysical properties of imaged objects. Invariance to ambient scene conditions, ability to classify objects and focus on phenomena affecting long-wave IR imagery are the special features of the method. Symmetric alphastable models can adequately model all these features. Results of real IR imagery and ground truth data are presented.

Analysis of the wavelet transform (WT) of deterministic self-similar signals reveals a relationship between the discrete Fourier transform of the discrete wavelet transform (DWT) and the DWT. A new algorithm identifies the self-similarity parameter and characterizes real chaotic signals in the presence of additive noise. The analysis shows WT as a promising tool for fractal signal analysis.

Novel estimators based on asymptotic extreme value theory, order statistics, and fractional lower order moments enable fast estimation for parameters of the alpha-stable impulsive interference model. These estimators are suitable for obtaining real-time signal processing algorithms. The performance of the estimators is theoretically analyzed and compared with maximum-likelihood estimators. Monte Carlo simulations provide the verification.

A new receiver for detecting and classifying signals with random phase in bivariate, isotropic, Cauchy noise has been proposed. A Monte-Carlo simulation reveals that this proposed Cauchy receiver has more efficient operating characteristic than the Gaussian receiver. This receiver is seen to be very robust in the entire class of bivariate isotropic symmetric alpha-stable impulsive noises.

Impulsive noise bursts in communication systems are traditionally handled by incorporating in the receiver a limiter which clips the received signal before integration. An empirical justification for this procedure is that it generally causes the signal-to-noise ratio to increase. Recently, very accurate models of impulsive noise were presented, based on the theory of symmetric [Alpha]-stable probability density functions. In this paper, we examine the performance of optimum receivers, designed to detect signals embedded in impulsive noise which is modeled as an infinite variance symmetric [Alpha]-stable process, and compare it against the performance of several suboptimum receivers. As a measure of receiver performance, we compute an asymptotic expression for the probability of error for each receiver and compare it to the probability of error calculated by extensive Monte-Carlo simulation. Keywords - Impulsive noise, [Alpha]-stable distribution, optimum receiver, suboptimum receivers.

We evaluate a recently proposed approach (1)-(3) to the inverse problem of detection of the presence and estimation of the location of a known object from data collected in a set of diffraction tomographic experiments. We verify on experimental data the validity of the filtered backpropagation algorithms developed in (1)-(3), demonstrate their robustness to modeling errors and to severe limitations in the angular coverage of the tomographic data, and illustrate a potential application in medical imaging of soft tissue.

Nonlinear tomographic reconstruction algorithms are developed for inversion of data measured in scattering experiments in which the complex phase of the wavefields is modeled by an arbitrarily large (possibly infinite) number of terms in the Rytov series. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual filtered backpropagation algorithm of Diffraction Tomography as the leading linear term. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry. Index Terms--Born series, diffraction tomography, inverse scattering, Rytov series, Volterra series.

Nonlinear tomographic reconstruction algorithms are developed for inversion of traveltime measurements in scattering experiments in which data models are derived from an arbitrarily large (possibly infinite) number of terms in the perturbation solution to the ray or eikonal equations. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual linear reconstruction algorithm of Traveltime Tomography as the leading term. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry. Index Terms--Eikonal equation, inverse scattering, ray tracing, traveltime tomography, Volterra series.

A translation property is derived describing the field scattered from a known buried object placed at distinct locations. The result is used to derive the optimum algorithm for detecting the known buried object and estimating its location from noisy scattered electromagnetic field measurements. Index Terms--Ground penetrating radar, Lippmann-Schwinger equation, object detection, wave scattering.

Biomarkers are measurements of biological variables that can determine a state of health. They consist of measuring a single variable or a combination of variables related to the state of health that these variables represent. Biomarkers can provide an early warning of a health problem in relation to an individual patient or group of patients, and thus trigger actions and lead to interventions. Nutritional biomarkers measure the biological consequences of one's diet. In our recent work, we have used machine learning to predict weight, metabolic syndrome and blood pressure, using blood-exam-based biomarkers. In the current work, we use extreme value theory to examine the significance of outliers in health data, with a focus on diet and the standard biochemistry profile. Specifically, we show that, using extreme value analysis and by applying a systemisation of the process, health trends can be predicted, and thus, health interventions can be (at least partially) automated. For that purpose, public access datasets have been used, which were retrieved from the National Health and Nutrition Examination Survey. The NHANES is a program of studies designed to assess the health and nutritional status of the population in the United States. In total, about 70,000 datapoints were analysed, covering about a decade's worth of observations. [ABSTRACT FROM AUTHOR]

The Born series was used as the data-generating model in scattering experiments to generalize the usual propagation transform of diffraction tomography into higher order propagation transforms.

The objective of this study was to improve user experience when appreciating visual artworks with soundscape music chosen by a deep neural network based on weakly supervised learning. We also propose a multi-faceted approach to measuring ambiguous concepts, such as the subjective fitness, implicit senses, immersion, and availability. We showed improvements in appreciation experience, such as the metaphorical and psychological transferability, time distortion, and cognitive absorption, with in-depth experiments involving 70 participants. Our test results were similar to those of "Bunker de Lumières: van Gogh", which is an immersive media artwork directed by Gianfranco lannuzzi; the fitness scores of our system and "Bunker de Lumières: van Gogh" were 3.68/5 and 3.81/5, respectively. Moreover, the concordance of implicit senses between artworks and classical music was measured to be 0.88%, and the time distortion and cognitive absorption improved during the immersion. Finally, the proposed method obtained a subjective satisfaction score of 3.53/5 in the evaluation of its usability. Our proposed method can also help spread soundscape-based media art by supporting traditional soundscape design. Furthermore, we hope that our proposed method will help people with visual impairments to appreciate artworks through its application to a multi-modal media art guide platform. [ABSTRACT FROM AUTHOR]

Soft robots present an avenue toward unprecedented societal acceptance, utility in populated environments, and direct interaction with humans. However, the compliance that makes them attractive also makes soft robots difficult to control. We present two low-cost approaches to control the motion of soft actuators in applications common in human-interaction tasks. First, we present a passive impedance approach, which employs restriction to pneumatic channels to regulate the inflation/deflation rate of a pneumatic actuator and eliminate the overshoot/oscillation seen in many underdamped silicone-based soft actuators. Second, we present a visual servoing feedback control approach. We present an elastomeric pneumatic finger as an example system on which both methods are evaluated and compared to an uncontrolled underdamped actuator. We perturb the actuator and demonstrate its ability to increase distal curvature around the obstacle and maintain the desired end position. In this approach, we use the continuum deformation characteristic of soft actuators as an advantage for control rather than a problem to be minimized. With their low cost and complexity, these techniques present great opportunity for soft robots to improve human–robot interaction. [ABSTRACT FROM AUTHOR]

Item Response Theory (IRT) evaluates, on the same scale, examinees who take different tests. It requires the linkage of examinees' ability scores as estimated from different tests. However, the IRT linkage techniques assume independently random sampling of examinees' abilities from a standard normal distribution. Because of this assumption, the linkage not only requires much labor to design, but it also has no guarantee of optimality. To resolve that shortcoming, this study proposes a novel IRT based on deep learning, Deep-IRT, which requires no assumption of randomly sampled examinees' abilities from a distribution. Experiment results demonstrate that Deep-IRT estimates examinees' abilities more accurately than the traditional IRT does. Moreover, Deep-IRT can express actual examinees' ability distributions flexibly, not merely following the standard normal distribution assumed for traditional IRT. Furthermore, the results show that Deep-IRT more accurately predicts examinee responses to unknown items from the examinee's own past response histories than IRT does. [ABSTRACT FROM AUTHOR]

Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When building a deep neural network, the integrated approach hides the properties of individual subtasks, which can hinder finding the optimal configurations for the understanding task in academia. In this paper, we propose a problem definition for explicitly understanding knowledge in a line chart and provide an algorithm for generating supervised data that are easy to share and scale-up. To introduce the properties of the definition and data, we set well-known and modified convolutional neural networks and evaluate their performance on real and synthetic datasets for qualitative and quantitative analyses. In the results, the knowledge is explicitly extracted and the generated synthetic data show patterns similar to human-labeled data. This work is expected to provide a separate and scalable environment to enhance research into technical document understanding. [ABSTRACT FROM AUTHOR]

The development of technology brings the computerization of everyday life. The most common device used to communicate with a computer is the keyboard. Many papers have been devoted to the study of computer keyboards and virtual keyboards. The authors of this study propose a new concept of a single line keyboard named One Row Keyboard (ORK) which can be applied for both computer and virtual keyboards. Applying the proposed concept enables the number of keys on the keyboards to be reduced significantly. This allows for the development of keyboards even on really small mobile devices. The article also presents a new method of assigning characters to keys for the ORK and shows the use of this method to create exemplary ORK layouts. One Row Keyboard is also the first concept that makes it possible to create a fully alternating keyboard. Noteworthy is that the criterion of maximizing typing alternation is the most popular criterion for keyboard optimization used in the studies devoted to computer keyboards as it leads to an increase in typing speed and decrease in the number of errors. [ABSTRACT FROM AUTHOR]

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method. [ABSTRACT FROM AUTHOR]

We will increasingly become dependent on automation to support our manufacturing and daily living, and robots are likely to take an important place in this. Unfortunately, currently not all the robots are accessible for all users. This is due to the different characteristics of users, as users with visual, hearing, motor or cognitive disabilities were not considered during the design, implementation or interaction phase, causing accessibility barriers to users who have limitations. This research presents a proposal for accessibility guidelines for human-robot interaction (HRI). The guidelines have been evaluated by seventeen HRI designers and/or developers. A questionnaire of nine five-point Likert Scale questions and 6 open-ended questions was developed to evaluate the proposed guidelines for developers and designers, in terms of four main factors: usability, social acceptance, user experience and social impact. The questions act as indicators for each factor. The majority (15 of 17 participants) agreed that the guidelines are helpful for them to design and implement accessible robot interfaces and applications. Some of them had considered some ad hoc guidelines in their design practice, but none of them showed awareness of or had applied all the proposed guidelines in their design practice, 72% of the proposed guidelines have been applied by less than or equal to 8 participants for each guideline. Moreover, 16 of 17 participants would use the proposed guidelines in their future robot designs or evaluation. The participants recommended the importance of aligning the proposed guidelines with safety requirements, environment of interaction (indoor or outdoor), cost and users' expectations. [ABSTRACT FROM AUTHOR]

Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a strategy based on a genetic algorithm approach for achieving optimal collaborative learning groups, considering the students' personality traits as grouping criteria. A controlled experiment was designed with 238 students, quantifying their personality traits through the "big five inventory" (BFI), forming working groups and developing a collaborative activity in programming and related courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students applying the proposed approach with those obtained through other group formation strategies. The highlight of the study is that those groups whose formation was pre-established by the teachers through the proposed strategy have generally had a better collaborative performance than the groups with traditional formation, except in the case of heterogeneous formation, at the time of developing a collaborative activity. In addition, through the experiment, it was found that not considering criteria related to personality traits before the group formation generally led to lower results. [ABSTRACT FROM AUTHOR]