Miracle-dz/UniFAR-FTUs huggingface dataset:
Test generation algorithm for the All-Transition-State criteria of Finite State Machines

In the current article a novel test generation algorithm is presented for deterministic finite state machine specifications based on the recently introduced All-Transition-State criteria. The size of the resulting test suite and the time required for test suite generation are investigated through analytical and practical analyses and are also compared to the Transition Tour, Harmonized State Identifiers and random walk test generation methods. The fault detection capabilities of the different approaches are also investigated with simulations applying randomly injected transfer faults.
Robust Visual Reasoning via Language Guided Neural Module Networks

Neural module networks (NMN) are a popular approach for solving multi-modal tasks such as visual question answering (VQA) and visual referring expression recognition (REF). A key limitation in prior implementations of NMN is that the neural modules do not effectively capture the association between the visual input and the relevant neighbourhood context of the textual input. This limits their generalizability. For instance, NMN fail to understand new concepts such as “yellow sphere to the left" even when it is a combination of known concepts from train data: “blue sphere", “yellow cube", and “metallic cube to the left". In this paper, we address this limitation by introducing a language-guided adaptive convolution layer (LG-Conv) into NMN, in which the ﬁlter weights of convolutions are explicitly multiplied with a spatially varying language-guided kernel. Our model allows the neural module to adaptively co-attend over potential objects of interest from the visual and textual inputs. Extensive experiments on VQA and REF tasks demonstrate the effectiveness of our approach. Additionally, we propose a new challenging out-of-distribution test split for REF task, which we call C3-Ref+, for explicitly evaluating the NMN’s ability to generalize well to adversarial perturbations and unseen combinations of known concepts. Experiments on C3-Ref+ further demonstrate the generalization capabilities of our approach.
Using Cognitive Work Analysis and a Persuasive Design Approach to Create Effective Blood Pressure Management Systems

When developing patient self-management applications, designs must motivate patients to engage with the intervention so as to increase their medical adherence. This paper hypothesizes that for the analysis and design of these technologies, a Cognitive Work Analysis (CWA) approach would benefit from the additional insight provided by a Persuasive Design (PD) framework. The results of this combined approach are shown to be valuable in informing an Ecological Interface Design (EID) of self-management systems. Benefits come in two ways: First, by improving patients’ understanding of their disease and how to manage it, and second by increasing their motivation to self-monitor. The next step of this research will be to devise a set of design guidelines that can be used to create effective patient self-management systems.
Approving Measurement Systems when Using Derived Values

This article presents a methodology for approving a measurement system when the variable of interest is not directly measured. Rather, the manufacturer measures some other related variables to derive or predict the quality characteristic. To approve the measurement system, one must estimate error in each measured value and then, using either variable transformation or a Taylor series approximation, propagate these errors through the calculation to obtain an error distribution for the derived values. An evaluation of a measurement system used to measure the friction of automotive brake pads—derived from force and torque measurements—demonstrates the methodology.
Post Post-Broadcast Democracy? News Exposure in the Age of Online Intermediaries

nline intermediaries such as social network sites or search engines are playing an increasingly central role in democracy by acting as mediators between information producers and citizens. Academic and public commentators have raised persistent concerns that algorithmic recommender systems would negatively affect the provision of political information by tailoring content to the predispositions and entertainment preferences of users. At the same time, recent research indicates that intermediaries foster exposure to news that people would not use as part of their regular media diets. This study investigates these unresolved questions by combining the web browsing histories and survey responses of more than 7,000 participants from six major democracies. The analysis shows that despite generally low levels of news use, using online intermediaries fosters exposure to nonpolitical and political news across countries and personal characteristics. The findings have implications for scholarly and public debates on the challenges that high-choice digital media environments pose to democracy L iberal democracy is facing threats around the globe. In many academic and public accounts of the current perils of democracy, the high-choice media environment brought about by the Internet is prominently mentioned among the culprits. While mass broadcasters and newspapers have traditionally guaranteed
Differences between plasma and CSF p-tau181 and p-tau231 in early Alzheimer s disease

Plasma phosphorylated tau species have been recently proposed as peripheral markers of Alzheimer s disease pathology. In this cross-sectional study incuding ninety-one subjects, plasma p-tau181 and p-tau231 levels were elevated in the early symptomatic stages of AD, with similar levels than those of CSF. Plasma p-tau231 and p-tau181 were strongly related to CSF tau and amyloid and exhibited a high accuracy, close to CSF p-tau231 and p-tau181, to identify AD already in the early stage of the disease. The findings might support the use as diagnostic and prognostic peripheral AD biomarkers in both research and clinical settings.
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems

Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in developing efficient methods for \textit{smooth} low-rank optimization problems that avoid maintaining high-rank matrices and computing expensive high-rank SVDs, advances for nonsmooth problems have been slow paced. In this paper we consider standard convex relaxations for such problems. Mainly, we prove that under a natural \textit{generalized strict complementarity} condition and under the relatively mild assumption that the nonsmooth objective can be written as a maximum of smooth functions, the \textit{extragradient method}, when initialized with a"warm-start"point, converges to an optimal solution with rate $O(1/t)$ while requiring only two \textit{low-rank} SVDs per iteration. We give a precise trade-off between the rank of the SVDs required and the radius of the ball in which we need to initialize the method. We support our theoretical results with empirical experiments on several nonsmooth low-rank matrix recovery tasks, demonstrating that using simple initializations, the extragradient method produces exactly the same iterates when full-rank SVDs are replaced with SVDs of rank that matches the rank of the (low-rank) ground-truth matrix to be recovered.
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons

We study the sample complexity (i.e., the number of comparisons needed) bounds for actively ranking a set of n items from multi-wise comparisons. Here, a multi-wise comparison takes m items as input and returns a (noisy) result about the best item (the winner feedback) or the order of these items (the full-ranking feedback). We consider two basic ranking problems: top-k items selection and full ranking. Unlike previous works that study ranking from multi-wise comparisons, in this paper, we do not require any parametric model or assumption and work on the fundamental setting where each comparison returns the correct result with probability 1 or a certain probability larger than 12 . This paper helps understand whether and to what degree utilizing multi-wise comparisons can reduce the sample complexity for the ranking problems compared to ranking from pairwise comparisons. Specifically, under the winner feedback setting, one can reduce the sample complexity for top-k selection up to an m factor and that for full ranking up to a log m factor. Under the full-ranking feedback setting, one can reduce the sample complexity for top-k selection up to an m factor and that for full ranking up to an m log m factor. We also conduct numerical simulations to confirm our theoretical results.
The Applications of Machine Learning in Education Science Research

: The article presents an overview of the application of machine learning techniques in education science research. The research process shows the use of technology in learning and teaching, collecting information, analyzing and processing data to provide high-accuracy answers or advice in solving educational issues is the trend and strength in education science research. Through this, the authors make recommendations on some research directions in the field of education approaching international publications
Drowsiness Monitoring with EEG-Based MEMS Biosensing Technologies

Electroencephalography (EEG) has been widely adopted to monitor changes in cognitive states, particularly stages of sleep, as EEG recordings contain a wealth of information reflecting changes in alertness and sleepiness. In this study, silicon dry electrodes based on Micro-Electro-Mechanical Systems (MEMS) were developed to bring high-quality EEG acquisition to operational workplaces. They have superior conductivity performance, large signal intensity, and are smaller in size than conventional (wet) electrodes. An EEG-based drowsiness estimation system consisting of a dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression was developed to estimate drivers’ drowsiness levels in a virtual-reality-based dynamic driving simulator. The proposed system can help elders who are often affected by periods of tiredness and fatigue.
Bender Elements: Performance and Signal Interpretation

Bender elements are convenient shear wave transducers for instrumenting soil cells due to optimal soil-transducer coupling and compatible operating frequency. Experimental and analytical methods are implemented in this study to explore various aspects of bender element installations including: electromagnetic coupling prevention, directivity, resonant frequency, detection of first arrival, and near field effects. It is shown that electromagnetic coupling effects are critical in soils with high electrical conductivity and can be minimized by shielding and grounding, or by using parallel-type bender elements. Bender elements generate both P- and S-waves. The in-plane S-wave directivity is quasicircular. The resonant frequency of bender element installations depends on the geometry of the bender element, the anchor efficiency, and the soil stiffness. The cross correlation of subsequent reflections is a self-healing measurement procedure which resolves uncertainties in both travel time and travel distance. Near field effects can be effectively taken into consideration by matching the measured signal with the analytical solution, directly rendering shear wave velocity.
Predicting Future Position From Natural Walking and Eye Movements with Machine Learning

The prediction of human locomotion behavior is a complex task based on data from the given environment and the user. In this study, we trained multiple machine learning models to investigate if data from contemporary virtual reality hardware enables long- and short-term locomotion predictions. To create our data set, 18 participants walked through a virtual environment with different tasks. The recorded positional, orientation- and eye-tracking data was used to train an LSTM model predicting the future walking target. We distinguished between short-term predictions of 50ms and longterm predictions of 2.5 seconds. Moreover, we evaluated GRUs, sequence-to-sequence prediction, and Bayesian model weights. Our results showed that the best short-term model was the LSTM using positional and orientation data with a mean error of 5.14 mm. The best long-term model was the LSTM using positional, orientation and eye-tracking data with a mean error of 65.73 cm. Gaze data offered the greatest predictive utility for long-term predictions of short distances. Our findings indicate that an LSTM model can be used to predict walking paths in VR. Moreover, our results suggest that eye-tracking data provides an advantage for this task.
Modeling of high-fidelity synthetic imagery for defense applications

Within the military environment there is a growing interest in the use of synthetic imagery as a method for assessing the performance potential of many assets, such as camouflage systems, ATR/I systems, future sensor systems and the susceptibility of such systems to countermeasures. Field trials are expensive, subject to the vagaries of weather, and cannot be used to design or assess new systems and technologies. The requirements for high fidelity synthetic image simulation differ quite significantly dependent on the application area. The Camouflage Electro-Optic Simulation System (CAMEO-SIM) has been developed as a modular system to deliver 0.4-20 micron2 32-bit physics based synthetic imagery to defense researchers in the UK based on a 3D textured geometric representation of the synthetic environment. The system has been developed to be sufficiently flexible to support a wide range of defense applications requiring synthetic image streams as their inputs. The key difference between the CAMEO-SIM system and commercial image generation systems is that CAMEO-SIM is a first principle simulator working in spectral radiometric space solving the underlying physical equations of radiation transport. This difference underpins the military usefulness of the system and also enables the system to become predictive in nature. Using CAMEO-SIM, the user can examine the effects of changes in input parameters such as time of day, weather, material properties (optical and thermophysical), and wavelength from one set of geometric and textural information to assess the effects on the performance of the defense function being researched. If this goal can be achieved more robust defense function performance, due to larger available training sets or better understanding of the issues at the start of the development, will ensue. This paper outlines the development status of the CAMEO-SIM system and describes the current enhancements undertaken to improve the fidelity and accuracy of the system for more recent defense requirements. The paper also outlines the currently identified areas of development to provide improved capability. The paper also discusses the interaction between the CAMEO-SIM system and the Fidelity Investigation and Reporting Environment (FIRE) system. This enables the modeler to examine early in the modeling process the quality of the textural and geometric information used in the synthetic imagery.
Modeling Multiple Influences on Vocabulary Acquisition: Context, Symbol, and Association Learning

The goal of this paper is to provide a theoretical taxonomy of the contribution of a variety of potential influences on children’s vocabulary acquisition. The variables analyzed in this study include parental input frequency, articulation difficulty, iconicity, and concreteness (imageability). Within the framework of the Fuzzy Logical Model of Perception (FLMP), it is assumed that there are three important components in the acquisition of new vocabulary: contextual or stimulus learning, symbolic or response learning, and the association between the context and symbol. It is further assumed that parental input drives the learning process, concreteness influences context learning, articulation difficulty influences symbol learning, and iconicity influences learning the association between the context and symbol. Previous research on children’s expressive vocabulary has shown strong effects of all four variables across the first four years of life. Parental input has the largest influence, articulation difficulty negative impacts word production, and the positive influence of iconicity and concreteness decrease systematically with increasing age. The relative independence of these influences is interpreted as support for the FLMP theoretical framework.
Who Really Knows about Reclining the Passenger Seat?

Warnings are important for several reasons. They provide information about hazards to those who may be unaware of them. They may also serve as reminders about hazards in circumstances where the hazard may be forgotten, and they present information about how to avoid the hazards warned against. Some hazards are deemed open and obvious because they are presumed to be either known to all who might encounter them or people are presumed to recognize their risks by their characteristics. The present research considers a hazard (reclining the automobile passenger seat) that might seem to be obvious. One report indicated this might be the case. However, more recent research has shown it is not the case. This research examines the fact that different procedures produce different estimates of how well understood the hazard is. The conclusion is that it is important to consider experimental techniques in evaluating results.
DENSENET CONVOLUTIONAL NEURAL NETWORK APPLICATION FOR PREDICTION OF COVID-19 USING CT SCAN IMAGES

: Now days human health and lives are recently impacted by Coronavirus. Due to the unavailable proper vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection spreading (e.g., by isolating the patients). This situation indicates to improve auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a mostly used technique for pneumonia because of its common availability. The application of DenseNet CNN systems integrated with images can be a promising alternative for the identification of COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on DenseNet is the updated CNN architecture in the present state to detect COVID-19. The results outperformed 89% accuracy, with 90% recall showing good performance for the identification of COVID-19.
Comparison and Analysis of Novel Score-Variance Portfolio Models based on Methods for Ranking Fuzzy Numbers

Ranking the fuzzy numbers plays an important role in fuzzy decision-making, but it is challenging to rank multiple fuzzy numbers comprehensively and effectively. In previous studies, various properties and factors of fuzzy numbers are considered to construct the ranking scoring formula. Therefore, in theory, the uncertainty of fuzzy numbers can be better defuzzified, and the formula can measure the characteristics of fuzzy numbers. In application, it is meaningful and valuable to investigate the use of fuzzy number’s ranking score in fuzzy portfolio to measure characteristics of the asset. Based on the classical mean-variance (M-V) model and four kinds of ranking score formulas, this paper constructs the score-variance (S-V) models. Finally, a numerical experiment is presented to illustrate the feasibility and validity of our proposed S-V model. Compared with the M-V model, on one hand the results of our proposed models are generally consistent with the classic M-V model; on the other hand, the return of our proposed model is slightly smaller in some part, but it performs better in skewness and Sharpe Ratio.
SU-8 thick photoresist processing as a functional material for MEMS applications

The use of SU-8 high aspect ratio, thick, photoresist as a functional material for MEMS applications is described in this paper. SU-8 processing is developed to implement low-stress SU-8 structures as permanent and functional material incorporated with silicon-on-insulator technologies. Silicon micromachined cantilevers were fabricated with SU-8 structures on the cantilevers as added masses. Separation of material function can be achieved in this way. Silicon provides excellent mechanical properties, while SU-8 is used as extra mass to adjust the mechanical behaviour. The resonance behaviour of the cantilever structure with SU-8 is characterized through measurement, simulation and calculation, and the strength of the SU-8 material for this purpose is evaluated. The results show that SU-8 is well suited as a permanent material in mechanically active MEMS devices, and several applications are suggested. 3D MEMS architectures can also be achieved in this manner.
A control method to reduce the standard deviation of flow time in wafer fabrication

This paper proposes a control, method for reducing flow time variability in a wafer fabrication facility with multiple wafer types. We employ stochastic Petri nets to model and analyze the machine module, and define operation due dates using a novel utilization index metric. An operation due date (OPNDD) rule for lot dispatch is proposed and evaluated against other lot dispatch policies.
Noise source modeling for cyclostationary noise analysis in large-signal device operation

Starting from the analysis of fundamental noise sources in large-signal (LS) periodic operation, a system theory approach is proposed for the modeling of colored noise sources in devices and circuits driven in LS conditions. According to this interpretation, colored sources are generated by low-pass filtering and amplitude modulation of a white unit Gaussian process. The order of the modulation and filtering steps leads to two different (albeit coincident in small-signal conditions) LS noise sources. Through the analysis of GR noise in physics-based device simulation, it is shown that only one modulation scheme (based on low-pass filtering followed by amplitude modulation) is consistent with the fundamental approach. This result also applies to 1/f noise mechanisms when 1/f fluctuations derive from population fluctuations.
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks

For many applications in image analysis, learning models that are invariant to translations and rotations is paramount. This is the case, for example, in medical imaging where the objects of interest can appear at arbitrary positions, with arbitrary orientations. As of today, Convolutional Neural Networks (CNN) are one of the most powerful tools for image analysis. They achieve, thanks to convolutions, an invariance with respect to translations. In this work, we present a new type of convolutional layer that takes advantage of Bessel functions, well known in physics, to build Bessel-CNNs (B-CNNs) that are invariant to all the continuous set of possible rotation angles by design.