Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-Generating Business Process Model From Heterogeneous Documents: A Comprehensive Literature Survey

Automatically generated business processes can be sourced from documents in the form of structured text or natural language. Although there have been several studies discussing generating business processes, there is still no research using systematically reviews as the source documents. This study presents a systematic literature review on document sources, methods, and challenges in generating business processes. We conducted a systematic literature review published from 2017 to early 2022 and identified 24 main studies discussing the sources of documents in generating business processes. We formulated and applied inclusion and exclusion criteria in two stages to determine the most relevant studies for our research goal. This literature review found that the most frequently used document sources were textual business rules (structured rules), using case diagrams (14 main studies), event logs (7 studies), and natural language text (3 studies) including customer feedback. In the aspect of the method, the most widely used method is in the field is natural language processing (NLP), followed by other methods such as semantic knowledge engineering (SKE), fuzzy, graph-based, deep learning, and the combination of NLP with deep learning. Meanwhile, the challenges faced in generating business processes include text preprocessing or document extraction, integration of business rules with business processes, and challenges in the form of time interval constraints, activity sequences, dummy activities, or invisible tasks that are generally found in event logs.
Enhanced Physical Layer Secure Key Generation using mm Wave Beamforming

Secret key generation based on wireless channel reciprocity is a promising area of research towards ensuring secure wireless communication. In this paper, we propose a method of generating secret keys using channel diversity obtained via millimeter wave (mmWave) beamforming at 60 GHz. The pro-posed method is simulated using the QuaDRiGa channel model considering an industrial nonline of sight (NLOS) environment. We verify the method by conducting experiments using a 60 GHz software-defined-radio (SDR) communication system with a linear phased-array-antenna of 16 dual patch elements. Further, we present a composite key generation method in which both the real and imaginary parts of the channel state information (CSI) are utilised, and several sub-keys obtained from individual beam directions are appended. As a result, we can generate long composite keys with high entropy and secrecy. The keys randomness are assessed employing the National Institute of Standards and Technology (NIST) statistical test suite, showing the generated keys are highly random.
Deep Reinforcement Learning-based Task Offloading and Resource Allocation in MEC-enabled Wireless Networks

Mobile edge computing (MEC) has recently become an enabling technology for mobile operators that are offering a diverse set of services. These services require extensive storage, energy, and computation resources. However, user devices (UDs) have resource constraint to meet the requirements of such services. To tackle the contradiction between resource-constrained UDs and computationally intensive services, MEC have been proposed. MEC servers provide task execution services for UDs. On receiving a service request from the UDs, a MEC server within the network may dynamically allocate computation and memory resources for the task execution. As the MEC servers have limited capacity, efficient utilization of MEC resources is necessitated. Also, it is challenging to find an optimal solution for efficient resource allocations due to different task requirements for a diverse set of services offered to users and dynamicity in wireless networks. To address these problems, we propose a partial task offloading and resource allocation scheme to maximize user task completion within a tolerable time period while minimizing energy consumption. In this paper, we convert the formulated optimization problem to a markov decision process (MDP) and then propose a solution based on the deep deterministic policy gradient (DDPG) algorithm. The performance results show that the proposed method completes a greater number of tasks within a tolerable delay and reduces the energy consumption in the network, compared to those of other conventional schemes.
A Power-Efficient Source-Follower Based Tunable Pseudo-RC Low-Pass Filter for Wearable Biomedical Applications

Most neural signal monitoring front-ends rely on the low-pass filter (LPF) to filter out the out-of-band interference and noise. A large time constant is required for such filters due to the low-frequency characteristic of neural signals. MOSFETs operating in the sub-threshold region (pseudo resistors) are widely used as resistors due to their large impedance with a small die area, which shows the potential to realize a considerable time constant LPF with acceptable capacitors. However, its linearity and impedance are limited by the applied voltage and suffer from process, voltage and temperature (PVT) variations. In this work, a subthreshold-source-follower (SSF) based LPF with self-biased pseudo-resistors (PRs) is presented. The self-biased scheme for PRs substantially increases the linearity and impedance without extra area consumption and results in a tunable low cut-off frequency. The proposed circuit is designed and simulated in the TSMC 40-nm CMOS process. Simulation results show that it can achieve a DC gain of 0 dB, an input-referred noise (IRN) of 44 µVrms, a THD@1 Hz of 0.49%, a die area of 0.008 mm2 and a low cut-off frequency of 82 Hz while the power consumption is 16.1 nW. Performances achieved are comparable with the state of the art.
Estimating Heart Rate from Seismocardiogram Signal using a Novel Deep Dominant Frequency Regressor and Domain Adversarial Training

Estimating heart rate (HR) from the seismocardiogram (SCG) signal can provide a more unobtrusive alternative for long-term HR monitoring where the gold standard electrocardiogram (ECG) signal is less favorable. Deep learning (DL) approaches have demonstrated promise in accomplishing this and are attractive due to their flexible and data-driven nature. However, current dense layer-based DL approaches lack a carefully designed regressor to estimate the HR in the SCG input and may overfit in low-data regime. It is also uncertain how well most of these DL approaches can generalize to unseen subjects, as evaluation has primarily taken place with no separation of subjects between training and testing data. In this work, we address these limitations by designing a DL approach for SCG HR estimation that leverages our proposed Dominant Frequency Regressor (DFR) with a Fast Fourier Transform layer and test the model performance using leave-one-subject-out cross validation. Specifically, we measure ECG and SCG from 19 subjects using our custom-built wearable patch. Here, ECG is used for training and regularization only, and SCG is used for training and inference. These signals are bandpass filtered, segmented into 60s windows, and normalized. Next, the proposed DFR-based DL model is applied and regularized by domain adversarial training. We report a mean absolute error (MAE) of 1.42±1.66 beats per minute (bpm) for HR with a range of 63-104 bpm. The proposed method can augment existing methods or be adapted for other problems of similar nature owning to its superiority to the dense layers-based alternatives. This work can lead to accurate, real-time algorithms for estimating 60s HR from a single-chest worn accelerometer only, which could be embedded in textiles.
Drone-Assisted Lane Change Maneuver using Reinforcement Learning with Dynamic Reward Function

This paper provides a Lane Change Assistance (LCA) platform that communicates with Unmanned Aerial Vehi-cles (UAV). The proposed platform is based on a reinforcement learning technique, where a Deep Q-Network (DQN) is trained to make lane change decisions. The reward function of the DQN agent considers safety, comfort and efficiency perspectives. Specifically, the safety reward, based on the road vehicular density, is adapted dynamically by the drone during the training phase. Performance analysis proves that the proposed platform improves the total travel time while reducing the collision rate and responding to urgent lane changes in a timely manner.
Asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects: Global minimization via Monte Carlo

The asymptotic behavior of the systems $X_{n + 1} = X_n + a_n b( {X_n ,\xi _n } ) + a_n \sigma ( X_n )\psi_n $ and $dy = \bar b( y )dt + \sqrt {a( t )} \sigma ( y )dw$ is studied, where $\{ {\psi _n } \}$ is i.i. d. Gaussian, $\{ \xi _n \}$ is a (correlated) bounded sequence of random variables and $a_n \approx A_0/\log (A_1 + n )$. Without $\{ \xi _n \}$, such algorithms are versions of the “simulated annealing” method for global optimization. When the objective function values can only be sampled via Monte Carlo, the discrete algorithm is a combination of stochastic approximation and simulated annealing. Our forms are appropriate. The $\{ \psi _n \}$ are the “annealing” variables, and $\{ \xi _n \}$ is the sampling noise. For large $A_0 $, a full asymptotic analysis is presented, via the theory of large deviations: Mean escape time (after arbitrary time n) from neighborhoods of stable sets of the algorithm, mean transition times (after arbitrary time n) from a neighborhood of one stable set to another, a.
Microwave Imaging: CPW Fed Microstrip Patch Antenna with Defected Ground Plane Operating in ISM Band for Breast Tumor Detection

A Coplanar Waveguide (CPW) fed patch antenna with defected ground plane operating in microwave range, i.e. license free 5.8 GHz ISM band is presented for breast tumor detection. The antenna offers a size of 50×50×1.2 mm3 on a Rogers RT5880 substrate material with a loss tangent of tanδ = 0.0009. The optimized antenna can successfully detect the presence of a tumor cell surrounding with healthy tissues in a breast phantom. The reflection coefficients (S11) of the proposed antenna with and without tumor are -24.11 dB and -23.96 dB respectively. The gain of the designed antenna remains 5.65 dB at 5.8 GHz with tumor cell. The proposed CPW fed microstrip patch antenna operating in license free ISM Band provides us a feasible solution to detect breast tumor successfully through microwave imaging at an early stage.
On the Second Order Statistics of Cooperative UAV Communications underlying Interference Limited Composite Fading Conditions

Unmanned-aerial-vehicles (UAVs) enabled communications (ECs) are intended to be an essential part of forthcoming beyond 5G (B5G) and 6G communication networks (CNs). UAV-to-ground (UG)-ECs in densely populated areas are usually exposed to composite fading conditions and the impact of co-channel interference (CCI). In this paper, we provide second order (SO)-metrics of UG communications over double-scattered, single-shadowed (DSc-SSh) fading channels in the interference limited environment, modeled as a ratio of the product of double a-µ and Gamma (G) random variables (RVs). Novel integral form formulas for average fade duration (AFD) and level crossing rate (LCR) of the proposed UG communication system over the composite channels in the presence of CCI are efficiently obtained and approximated by Laplace based integration (LBI)-method. Moreover, the system model is extended to investigate the SO-metrics of cooperative UAV transmission selection (TS) system that chooses the UG transmission link with the highest signal level among L independent UG links. The impact of different DSc-SSh fading conditions as well as the number of UAVs on the SO- metrics are well addressed.
Following the Metals in the Intergalactic and Circumgalactic Medium over Cosmic Time

The circumgalactic medium (CGM) of galaxies serves as a record of the influences of outflows and accretion that drive the evolution of galaxies. Feedback from star formation drives outflows that carry mass and metals away from galaxies to the CGM, while infall from the intergalactic medium (IGM) is thought to bring in fresh gas to fuel star formation. Such exchanges of matter between IGM-CGM-galaxies have proven critical to producing galaxy scaling relations in cosmological simulations that match observations. However, the nature of these processes, of the physics that drives outflows and accretion, and their evolution with cosmic time are not fully characterized. One approach to constraining these processes is to characterize the metal enrichment of gas around and beyond galaxies. Measurements of the metallicity distribution functions of CGM/IGM gas over cosmic time provide independent tests of cosmological simulations. We have made great progress over the last decade as direct result of a very sensitive, high-resolution space-based UV spectrograph and the rise of ground-based spectroscopic archives. We argue the next transformative leap to track CGM/IGM metals during the epoch of galaxy formation and transformation into quiescent galaxies will require 1) a larger space telescope with an even more sensitive high-resolution spectrograph covering both the far- and near-UV (1,000-3,000 \AA); and 2) ground-based archives housing science-ready data.
Real-Time Medical Radar-based Vital Sign Monitoring System Implemented with Signal Quality Classification Algorithm

Owing to the Covid-19 epidemic, medical radar has become a potential non-contact method in patient monitoring. However, this radar type is sensitive to external interference. The output signal obtained by the radar when a patient makes random body movements can significantly reduce the accuracy of vital sign detection algorithms. In addition, algorithms should be developed for actual application. In this study, we present an improved model of the 24-GHz radar signal quality classification system and a technique to enhance the resolution of respiration rate (RR) and heart rate (HR) for short time interval signals. Moreover, a complete system including signal quality assessment and vital signs extraction is implemented in real time on the Lab-VIEW software. The signal quality classification was evaluated on the measured signals of 10 healthy subjects. Accordingly, the obtained results indicate that with specific features, the accuracy of signal quality classification reaches 89.8%-100% while real-time RR and HR extraction results demonstrate significant agreement between radar measurement and the contact-type sensor.
Multi-Messenger Astronomy with Extremely Large Telescopes

The field of time-domain astrophysics has entered the era of Multi-messenger Astronomy (MMA). One key science goal for the next decade (and beyond) will be to characterize gravitational wave (GW) and neutrino sources using the next generation of Extremely Large Telescopes (ELTs). These studies will have a broad impact across astrophysics, informing our knowledge of the production and enrichment history of the heaviest chemical elements, constrain the dense matter equation of state, provide independent constraints on cosmology, increase our understanding of particle acceleration in shocks and jets, and study the lives of black holes in the universe. Future GW detectors will greatly improve their sensitivity during the coming decade, as will near-infrared telescopes capable of independently finding kilonovae from neutron star mergers. However, the electromagnetic counterparts to high-frequency (LIGO/Virgo band) GW sources will be distant and faint and thus demand ELT capabilities for characterization. ELTs will be important and necessary contributors to an advanced and complete multi-messenger network.
Stress Detection Using Eye Tracking Data: An Evaluation of Full Parameters

Stress has been a common disorder in human societies and numerous studies have been conducted on the early diagnosis of stress. Previous studies have shown that it is possible to diagnose stress using eye tracking data. This study aimed to obtain a new and significant method for detecting parameters of the eye tracker and electrodermal activity signal by discrimination of “stress” vs. “relaxation” and to achieve higher accuracy than previous research. We used a Stroop task and a mathematical stressor task in which stress elements were placed in a novel design to separate stress from relaxation in the Stroop task and evaluate three levels of stress in the mathematical task. In the present study, we recorded the eye tracking data of fifteen participants and thoroughly investigated the pupil diameter (PD) and electrodermal activity (EDA) features to discriminate different stress states. After preprocessing, several features were extracted and selected. Then, the features were used for classification by applying support vector machine, linear discriminant analysis, and k-nearest neighbor classifiers. The linear discriminant analysis classifier, for which the accuracy was 88.43% in the Stroop and 91.10% in the mathematical, showed higher accuracy than the other classifiers when using PD and EDA features. Also, PD features demonstrated more reliability and ability to differentiate stress from relaxation compared to traditional EDA.
Shear and Ellipticity in Gravitational Lenses

Galaxies modeled as singular isothermal ellipsoids with an axis ratio distribution similar to the observed axis ratio distribution of E and S0 galaxies are statistically consistent with both the observed numbers of two-image and four-image lenses and the inferred ellipticities of individual lenses. However, no four-image lens is well fitted by the model (typical χ2/Ndof ~ 20), the axis ratio of the model can be significantly different from that of the observed lens galaxy, and the major axes of the model and the galaxy may be slightly misaligned. We found that models with a second, independent, external shear axis could fit the data well (typical χ2/Ndof ~ 1), while adding the same number of extra parameters to the radial mass distribution does not produce such a dramatic improvement in the fit. An independent shear axis can be produced by misalignments between the luminous galaxy and its dark matter halo, or by external shear perturbations due to galaxies and clusters correlated with the primary lens or along the line of sight. We estimate that the external shear perturbations have no significant effect on the expected numbers of two-image and four-image lenses but that they can be important perturbations in individual lens models. However, the amplitudes of the external shears required to produce good fits are larger than our estimates for typical external shear perturbations (10%-15% shear instead of 1%-3%), suggesting that the origin of the extra angular structure must be intrinsic to the primary lens galaxy in most cases.
Deep Learning-Based Rate-Splitting Multiple Access for Massive MIMO-OFDM Systems With Imperfect CSIT

Due to the high dimensionality of the channel state information (CSI) in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, acquiring accurate CSI at the transmitter (CSIT) with limited feedback overhead is difficult, severely degrading the performance of conventional SDMA beamforming techniques. To this end, this paper proposes a deep learning (DL)-based end-to-end (E2E) rate-splitting multiple access (RSMA) beam-forming scheme for massive MIMO-OFDM systems, including an analog beamforming network (ABN) and a model-driven RSMA digital beamforming network (RDBN). We adopt an E2E training approach to jointly train the proposed ABN and MRBN to obtain better beamforming performance. Numerical results show that the proposed DL-based E2E RSMA beam-forming scheme significantly improves the system capacity and outperforms the state-of-the-art schemes.
Ultra-Wideband Steerable Antenna Array Based on EBG Structure for Millimeter Wave 5G Applications

An ultra-wideband miniature antenna array based on electromagnetic band gab (EBG) has been presented for the five generation of wireless communication (5G) applications. The proposed antenna array is fed by a 3dB hybrid coupler. Simulation results show that two beams steering toward ±13° with 7.35 dB maximum gain magnitude in E-plane at 28 GHz, are achieved. Thus, by using EBG structure, the proposed antenna array achieves an ultra-wideband (UWB) behavior, that covers 23.28-35.78 GHz (44.64%) frequency range.
A Simulation of Adsorption of Ampholytic Diblock Copolymers Confined in the Metal Crystal

The diblock copolymer chain was flexible in the simulation system. The copolymer chain adsorption on the face center cubic metal crystal surface was researched by MD simulation. The adsorption density has been distinguished by the ratio of A-block beads to B-block beads. The counterions were added to the system which carry the opposite charges to B-block beads. The results show that the strongly binding between B-block and counterions, formed clusters. The copolymer is adsorbed on the bottom surface, which formed a copolymer brush.
Design and Simulation of a Low Power 384-channel Actively Multiplexed Neural Interface

Brain computer interfaces (BCIs) provide clinical benefits including partial restoration of lost motor control, vision, speech, and hearing. A fundamental limitation of existing BCIs is their inability to span several areas (> cm2) of the cortex with fine (< 100 μm) resolution. One challenge of scaling neural interfaces is output wiring and connector sizes as each channel must be independently routed out of the brain. Time division multiplexing (TDM) overcomes this by enabling several channels to share the same output wire at the cost of added noise. This work leverages a 130-nm CMOS process and transfer printing to design and simulate a 384-channel actively multiplexed array, which minimizes noise by adding front end filtering and amplification to every electrode site (pixel). The pixels are 50 μm × 50 μm and enable recording of all 384 channels at 30 kHz with a gain of 22.3 dB, noise of 9.57 μV rms, bandwidth of 0.1 Hz – 10 kHz, while only consuming 0.63 μW/channel. This work can be applied broadly across neural interfaces to create high channel-count arrays and ultimately improve BCIs.
Comparative study of lightning activity over north vs south Equatorial Africa

The aims of this paper is to compare the lightning activity (number of lightning flashes) of mesoscale convective systems over North and South Equatorial Africa. It examines also the correlation between the lightning activity in the Northern Equatorial Africa and that in the Southern one. Indeed, spatial observations have shown that the highest concentrations of lightning on the Earth are mostly located in Equatorial Africa. This study has been made using data processing, descriptive statistical analysis and wavelet coherence analysis. The obtained results show that there is more lightning activity in the North region of Equatorial Africa than in the South one, in an average ratio of about 1.12 to 1. At the same time, a North-South regional contrast in lightning activity is observed over the Equatorial Africa. Concerning their relationships, it has been found that the lightning activity is strongly and negatively correlated (R=-0.588) between the two hemispheres. Furthermore, using the wavelet method we have obtained the same results. The degree of linearity between the lightning activity signals S1 of the Northern Equatorial Africa and S2 of the Southern one has been characterized (linear correlation level between 0 and 1) for any time-scale location and shows that the interactions between the two signals are at sub-annual and seasonal scales, with a relative phase difference of 180° (in phase opposition). Thus, the two signals are negatively correlated. Lightning activity shows very high trends in both sub-regions and the variation in its monthly as well as seasonal mean could be explained given the strong convection and thunderstorms throughout the region.