Miracle-dz/UniFAR-FTUs huggingface dataset:
Reversible Execution for Robustness in Embodied AI and Industrial Robots

Reversible computation is a computing paradigm where execution can progress backward as well as in the usual, forward direction. It has found applications in many areas of computer science, such as circuit design, programming languages, simulation, modeling of chemical reactions, debugging, and robotics. In this article, we give an overview of reversible computation focusing on its use in robotics. We present an example of programming industrial robots for assembly operations where we combine classical AI planning with reversibility and embodied AI to increase the robustness and versatility of industrial robots.
Codon Effect on the Entire Genome Based upon Genome-Wide Recoded Escherichia coli

Synonymous codon mutation can influence transcription and translation through gene expression. Researchers have studied how altering codons is linked to expression. However, they did not investigate the correlations at a whole genome level. We classified 1090 genes encoding proteins and analyzed their sequence properties in both strains of Escherichia coli with the entire genome recoded before and after codon alterations. It was observed that expression levels of the genes associated with cell membrane almost increase, while those related to protein production nearly decrease. Regarding to cytosolic metabolism, the overall expression of TCA cycle goes up, while glycolysis holds most genes with lessened expressions. The different values of A+T content, global and local codon usage, and stability of mRNA structure impacts expression ratios in an ambiguous degree, suggesting us take more parameters at cell level into account.
Performance Comparison of Controllers for the Autonomous Underwater Vehicle REMUS 100

Autonomous underwater vehicles have been found to be particularly useful for gathering data as well as other tasks such as building sub-sea infrastructure. Due to the uncertainty of the environment, and the nonlinear dynamics of the vehicles, controlling them is typically an involved task. Albeit a number of controller designs are possible, and several are available in the literature, it is unclear which designs are better suited for specific vehicles. In this paper, we design classical controllers, linear quadratic regulators and sliding mode controllers for the depth and steering subsystems of the Oceanographic Systems Laboratory’s REMUS 100. Based on the performances of these designs, we suggest appropriate controllers for the two subsystems.
An Algorithm for Locating PVC and SPB in Wearable ECGs

Wearable electrocardiogram devices are being developed rapidly as they can be used to monitor the user’s health in the long term. Their development is dependent on advances in automatic processing algorithms. Given that its intended use requires the wearable device to collect a large amount of data, and in light of the context where its hardware is limited by cost and power consumption, this paper proposes an algorithmic framework that can automatically identify position premature ventricular contraction (PVC) and supraventricular premature beat (SPB) at low computational cost by using 24-hour dynamic single-lead ECG recordings. We first use convolutional neural networks that require a small number of calculations to find problematic segments in the signal, and then use bidirectional long short-term memory networks to accurately locate the PVC and SPB. In addition to using the second-level structure that reduces the total amount of computation required by the task to 1/3.78, the algorithm has adopted focal loss to ensure the accuracy of classification of the disease samples. The algorithm was used in the Third China Physiological Signal Challenge (CPSC) 2020 and came in 10th place, which verified its effectiveness. We trained the model once again by using the data provided at CPSC 2020, and obtained the F1 scores of PVC and SPB were 0.6698 and 0.6111.
Development and Research of the Time Series Prediction Method Based on Finite State Automaton

In this article, we explore the possibility of using deterministic finite state machines to forecast real time series. The proposed method is based on a ten-headed automaton that allows recognizing multilinear sequences. This machine has been redesigned, modified, and implemented taking into account the time series. Theoretically, the resulting automaton takes into account the appearance of deviations in the data and assumes adaptation when the pattern changes. In addition to modification, the authors considered the possibility of machine failure by other forecasting methods (for example, forecasting methods based on archivers). The article briefly describes the basic version of the machine, as well as all applied modifications with the justification for their use. Each modification of the automaton is examined both on sequences similar to a multilinear pattern and on data of stochastic origin. In addition, the article presents the results of applying the proposed implementations for forecasting dollar exchange rates.
Radio Propagation Models for TDOA Localization Performance Evaluation Exploiting Ray Tracer

In this paper, a deterministic channel model based on ray tracing (RT) is utilized to evaluate how the wave propagation mechanisms influence the realistic localization systems. Three-dimensional (3D) information of a university campus scenario with dense buildings is used for trials. RT is utilized to generate channel impulse responses (CIRs) between the targets and positioning nodes (sensors) under three frequency bands with various propagation mechanisms, e.g., multiple reflection, scattering, diffraction, and their combinations. Therewith, the CIRs are utilized to estimate target locations. The significance of this work lies in: (1) providing a measurement-free approach which is able to effectively evaluate the localization performances of realistic environment; (2) revealing how environment models and wave propagation mechanisms influence the localization performances. The proposed method can be easily transferred to evaluate other localization methods and to optimize deployment of localization sensor networks.
Uncertainty-Aware Data-driven Tobacco Loosening and Conditioning Process Moisture Prediction and Control Optimization

Tobacco loosening and conditioning process is one of the most important processes in cigarette production, the quality of which affects the final product quality largely. The control of water proportion added in this process is human experience-centric and inconsistent without intensive human tuning. This paper proposes an uncertainty-aware outlet moisture prediction and optimization approach to provide updated water proportion to the PID controller for consistently desirable quality performance and stability. In this research, quantile regression is applied to describe the confidence of prediction, and Monte Carlo simulation is induced in the online optimization to deal with the uncertainty. The approach has been applied to a cigarette production line and the resulted performance showed significant improvement compared with the results of human tuning.
Towards a Constructive Framework for Control Theory

This work presents a framework for control theory based on constructive analysis to account for discrepancy between mathematical results and their implementation in a computer, also referred to as computational uncertainty. In control engineering, the latter is usually either neglected or considered submerged into some other type of uncertainty, such as system noise, and addressed within robust control. However, even robust control methods may be compromised when the mathematical objects involved in the respective algorithms fail to exist in exact form and subsequently fail to satisfy the required properties. For instance, in general stabilization using a control Lyapunov function, computational uncertainty may distort stability certificates or even destabilize the system despite robustness of the stabilization routine with regards to system, actuator and measurement noise. In fact, battling numerical problems in practical implementation of controllers is common among control engineers. Such observations indicate that computational uncertainty should indeed be addressed explicitly in controller synthesis and system analysis. The major contribution here is a fairly general framework for proof techniques in analysis and synthesis of control systems based on constructive analysis which explicitly states that every computation be doable only up to a finite precision thus accounting for computational uncertainty. A series of previous works is overviewed, including constructive system stability and stabilization, approximate optimal controls, eigenvalue problems, Caratheodory trajectories, measurable selectors. Additionally, a new constructive version of the Danskin’s theorem, which is crucial in adversarial defense, is presented.
Fuzzy Logic Controlled Active Hydro-Pneumatic Suspension Design Simulation and Comparison for Performance Analysis

This document evaluates the overall performance of an active hydro-pneumatic suspension system by comparison method for a special purpose vehicle structure. Firstly, the study includes mathematical modeling and simulation of both conventional and hydro-pneumatic suspension systems. The hydro-pneumatic suspension is a non-linear, complex system. The spring and damping characteristics for the system have been separately constructed. The control structure has been chosen to create a multivariate analysis by using the suspension deflection as a reference point to manipulate the active component of the suspension system with Fuzzy Control method. Finally, the study aims to create a better understanding of how the passive conventional, hydro-pneumatic, and active hydro-pneumatic suspension systems differ in means of comfort and vehicle handling issues.
Two-Gate FeFET SPICE Model and Its Application to Construction of Adaptive Adder

Production of matrices of silicon-on-insulator (SOI) synaptic two-gate ferroelectric transistors (2G FeFET) will allow to design and manufacture deep and recurrent neural networks (neuromorphic computations). Memorization and processing of information occurs in them not only in the same physical element, but also simultaneously and parallel in all cells with ultralow energy consumption. If we have not detailed knowledge of the device physics then phenomenological device models are used. A SPICE model of a two-gate ferroelectric transistor (2G FeFET) based on the use of its gate-drain current-voltage characteristic is proposed. An analytical approximation of the curves of the current-voltage characteristic is constructed. Fragments of the scaled sigmoidal function are used to approximate the current-voltage characteristic. Hysteresis is implemented using a Schmitt trigger, which switches the model between characteristic curves. Based on the 2G FeFET model, an adaptive adder model is built. It is shown that: the output signal of the adder is maximal when the input vector corresponds to the weights vector written into the adder; the adaptive adder is able to recognize distorted vectors corresponding to the reference vector (a property of the associative memory).
The Value Function with Regret Minimization Algorithm for Solving the Nash Equilibrium of Multi-Agent Stochastic Game

In this paper, we study the value function with regret minimization algorithm for solving the Nash equilibrium of multi-agent stochastic game (MASG). To begin with, the idea of regret minimization is introduced to the value function, and the value func-tionwithregretminimizationalgorithmisdesigned.Furthermore,weanalyzetheeffectofdiscountfactortotheexpectedpayoff. Finally,thesingle-agentstochasticgameandspatialprisoner’sdilemma(SDP)areinvestigatedinordertosupportthetheoreti-calresults.Thesimulationresultsshowthatwhenthetemptationparameterissmall,thecooperationstrategyisdominant;when thetemptationparameterislarge,thedefectionstrategyisdominant.Therefore,weimprovethelevelofcooperationbetween agentsbysettingappropriatetemptationparameters.
Analysis of the user experience in a web-based university staff’s publication management system

Research and analysis of user experience in software applications is a current area in the design and development of the user interface. Analysis of users’ behavior in a specific application can help detect vulnerabilities in the interface and direct developers to places to change. There are different approaches to data collection and analysis of user experience, as well as ready-made software environments that implement this. In this paper, we describe a particular approach to collecting, processing, and analyzing user behavior data in a web-based specific-task-oriented system. Information about the number of user sessions and clicks on various elements of the user interface is stored with the purpose of collecting enough historical data that can be further analyzed. The dataset examined in this paper consist of information about the sequences of actions of each user for five months. The dataset is unique because it contains user sessions right from the release of the application, giving the opportunity to examine the first responses of the users and to follow the development of their habits while working with it. The different groups of users (known in advance) and their behavior in the system are described. Conclusions are made about the benefits of changes to the interface and the added new features, as well as the way users perceive and use them.
Model-free adaptive control for MIMO nonlinear systems under packet dropout and DoS attack

The control problem for multiple-input multiple-output (MIMO) nonlinear systems against stochastically occurring packet dropout and periodic denial-of-service (DoS) jamming attacks is studied in this paper. Firstly, two independent Bernoulli distributions are applied to model random packet dropout and periodic DoS attack. Then combining with the model, a model-free adaptive control (MFAC) algorithm is put forward. Meanwhile, we analyze the stability of the algorithm theoretically and the results indicated that the tracking error is convergent under the presence of packet dropout and periodic DoS attack. Finally, a numerical example results verify the validity of the algorithm.
Algorithm of Solving for RF Circuit Equations by Using the Tensor Decompositions

One of the new approaches, which has a huge potential for reducing computational costs in the simulation of electronic circuits and CAD systems, is the tensor decomposition approach. Advances in recent years in the field of methods of tensor decompositions have significantly reduced memory for storage and reduced the computational time in solving high dimensional problems. Simulation of modern radio-frequency (RF) circuits, especially for RF integrated circuits, is one of the areas of extensive scientific research and development. The main problem of analysis methods for circuit design of RF microwave electronics is the significant computational costs associated with the high dimensions to be solved. This paper shows that the computational costs of circuit design are largely determined by the costs of solving a system of linear algebraic equations (SLAE). The number of equations (unknowns) can reach hundreds of thousands or more. Most of popular software tools for circuit simulation used both direct and iterative methods for solving the SLAE. The problems of these methods are indicated in the paper. Therefore, further development of methods for solving and reducing computational costs requires the use of new approaches, one of which is proposed in this paper and is associated with the use of tensor decompositions. The article presents the developed algorithm for converting a second-order matrix into a higher-order tensor. In the paper algorithms and software tools for tensor decompositions, as well as methods for solving SLAE in the representation of equations in tensor format are investigated. The results of numerical experiments and the results of comparison with traditional methods are presented.
The Influence of Vibro-Tactile Finger Stimulation Parameters on P300 Characteristics Changes for Rehabilitation: Pilot Study on Healthy Subjects

Up-to-date brain-computer interfaces based on the P300 neuromechanics most commonly apply visual and auditory stimulation. However, P300 based on vibro-tactile stimulation has a number of advantages for patients with visual diseases, photosensitivity epilepsy, and difficulty with gaze concentration. Such technologies can be used to improve the quality of life and neurorehabilitation of patients with stroke and movement disorders. The paper considers the possibility of recording the P300 evoked potential based on varying the parameters of vibro-tactile stimulation of the fingers with use vibration motors - the stimulus frequency of 1, 2, 4 Hz and the stimulus duration of 40, 80, 120 ms. The appearance of target stimulus probability is 11%. The amplitude and latency of the evoked tactile potentials P300 are evaluated. The dependence of the amplitude-temporal characteristics of the P300 in the dynamics of the experiment on the target-to-target interval is investigated. On the basis of the results obtained, a conclusion about the most preferable stimulation mode is made.
Generalized-reduced-order-extended-state-observer-based Adaptive Repetitive Control for Brushless DC Motor

This paper presents a method of designing a generalized-reduced-order-extended-state-observer-based (GROESO-based) adaptive repetitive control system for a brushless DC motor with time-varying parameter uncertainty and unknown nonlinear dynamics. In this system, GROESO-based control is used to estimate and compensate the mismatched nonlinear dynamics, model-reference adaptive control (MRAC) is used to suppress the matched parameter uncertainty, and repetitive controller undertakes the task of tracking periodic reference input signal. By appropriately choosing a disturbance compensation gain for GROESO-based control and adaptive parameters for MRAC, the influences of the nonlinear dynamics and parameter uncertainty are effectively attenuated. A feedforward compensator for repetitive control is also designed. Simulation results show that the designed brushless DC motor servo system achieves both satisfactory disturbance-rejection and tracking performances.
User Allocation for Real-Time Applications with State Sharing in Fog Computing Networks

Applications in which multiple users share the states in real-time over a network have been rapidly spreading, but network latency degrades their quality of service (QoS) and quality of experience (QoE). Although Fog computing effectively mitigates this problem, user allocation methods suitable for these applications with strict latency requirements have not yet been studied. Therefore, this paper proposes both offline and online methods that assume state sharing for user allocation in Fog environments. These methods not only reduce the mean of delays within each group composed of users who share the same states but also guarantee fairness among the users. The simulations demonstrate that our methods complete the allocation in a realistic time and outperform the baseline methods and architectures.
Improving viewing of webpages for users with assistive needs: insights into offline testing

In this paper, we discuss an application of computer vision for designing systems which can improve the online experience of users with assistive needs; we also delve further into the case of older adult users. We discuss some lessons learned of use for researchers aiming to validate their AI-driven algorithms for web improvements to be experienced by users. Our research is on depicting web pages as visual images, in order to facilitate decluttering and zooming of these pages for assistive needs users. In particular, we elaborate on the ability of our image segmentation model to support smart magnification of content for older adults, as well as a study we designed to examine the effectiveness of these improvements with real participants. We also discuss the possible promise of simulating user behaviour, in what we refer to as offline testing, as an important step in solidifying decisions about interfaces to use with end-users. In particular, this method can serve as a useful stand-in when the appropriate user base is elusive.
A Social Media Time-Series Data Analytics Approach for Digital Epidemiology

Various events and their perspectives around the world are discussed or posted at every moment on social media platforms like Twitter in near real-time, forming an enriched repository of information as historical records or time series. These include people’s sentiments, emotions, opinions, and other information such as situational aspects of the spreading of a particular disease, ailment, or a population explosion of some vectors or pathogens. Exploring and harnessing such information about a disease for surveillance to prevent and control its spreading or becoming epidemic or pandemic is worthwhile for a country or the world. In this paper, we correlate tweeting activity with the reported disease cases, and take advantage of the predictive power of neural networks and auto-regressive models to estimate disease incidences for the current week (aka nowcasting) considering the social media data and the disease case counts reported by the Government agencies. We propose Long Short-Term Memory (LSTM) network models and autoregressive moving average models with two channels of inputs to incorporate social media and historic disease case count data for predicting current disease case counts. We employ various LSTM network models and autoregressive moving average models to estimate the current week’s disease case count and compared their performance considering tweets as exogenous input to these models. The experimental results establish the efficacy of the LSTM network models with dynamically merged inputs for predicting disease case count with least prediction error.
Has the extreme risk spillover among the currencies along Belt and Road increased since COVID-19?

The international financial market has been fluctuating dramatically ever since the outbreak of the COVID-19 epidemic. The Belt and Road, as an international cooperation platform, could also be affected by the increasing risk factors during the epidemic period. This paper researched the extreme co-movement probability between 5 representative currencies along the Belt and Road and CNY before and after the epidemic with the tail dependence of the currency pairs based on copula. The results suggest that EUR’s and TRY’s tail dependence with CNY increased significantly while ZAR’s tail dependence with CNY decreased. Therefore, there does not exist a systematic increase of the extreme risk spillover. These findings on dependence structure between currencies along B& R could provide additional insight for policy makers and corporations.