Miracle-dz/UniFAR-FTUs huggingface dataset:
A STUDY ON NEURO FUZZY ALGORITHM IMPLEMENTATION ON BCI-UAV CONTROL SYSTEMS

Brain-Computer Interface (BCI) machines are capable of obtaining brain activities by conducting Electroencephalogram tests. Developments on both BCI and Machine Learning allowed various researchers to develop and study various BCI control systems, mainly varying with the algorithm implementation. This research presents a performance analysis of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for BCI control systems for drone maneuverability. Eye gestures were used to generate the EEG data that were captured using the Emotiv INSIGHT Neuroheadset. The obtained data were transferred to the computing hardware using IEEE 802.15 wirless communication protocol (i.e. Bluetooth connectivity); the data are processed using the 5th order Butterworth Band-Pass filtering and heuristic filtering. The filtered dataset is then fed to the ANFIS and a Support Vector Machine (SVM) algorithm, the latter serving as the basis, for training and quadcopter control implementation. Three flight tests were done, hover test, flight command test, and the flight control test, the final test compared the performance of the BCI control system using the ANFIS algorithm to the performance of a traditional handheld remote controller. Results from the initial two tests showed that the ANFIS performed comparably with the SVM, and even about 2% better. The final test showed that the BCI control system had a maximum variance of 4% compared to the handheld controller, where the latter served as the basis. It was found that between Machine Learning algorithms, ANFIS is as capable as the SVM for BCI control systems. Further developments may focus on employing time-series EEG preprocessing techniques.
Urn sampling, interval censoring and proportional hazard models

This paper proposes a new distribution-free statistical method for testing hypotheses about covariates for survival data having simultaneously right-, left- and interval-censored survival times. The new test is motivated by the analogue between urn sampling and the Cox’s proportional hazard models. Investigations of the significance levels and power as a function of the proportion of interval-censored observations and interval length show that the test performs well for most censoring situations encountered in practice. Simulation results also suggest that there is negligible loss of power in the practical situation in which the mean interval length for interval-censored observations is less than the mean survival time. This holds even with heavy interval censoring. Comparison with the widely used Mantel’s method for comparing two groups shows that the power of the new method appears to be superior. Furthermore, the test is relatively simple to carry out and generalizes to comparing k populations as well as the testing of general linear hypothesis for arbitrary covariates.
A new result on eigenvalue assignment by means of dyadic output feedback

A novel procedure is developed for establishing the extent to which the eigenvalues of the nth-order, m-input, l-output linear system dx/dt= Ax+Bu, y = Dx can be assigned by means of a dyadic output feedback law of the form u = u 0 + qp T. y. The treatment is based on an n × ml matrix G formed from the sequence of matrices DAtB (i = 0, 1,…, n − 1). An upper limit to the number of eigenvalues which can be assigned is determined from the rank r of G. and from a study of the possibility of real r × I solutions ∊ to an equation of the form [∊−Tμ/μ]=R T[p⊗q] where R T and T arise from the permutation GR=[G∗. G∗T], with G∗ n × r of rank r.
Limits of Liouvillian Functions

We are concerned with the calculation of limits of real-valued functions, and more generally with their asymptotic growth. One source of difficulties is that, for example, two large functions may cancel to give a much smaller function. We present a general method for handling such cancellation problems. As is normal in this area, we have to assume the existence of an oracle which determines the sign of any constants that we meet. On that basis we show how to compute the asymptotic forms of real Liouvillian functions. That is to say, elements of a field given by a tower of extensions of the basic constants by integrals, exponentials and real algebraic functions. Our method centres on the concept of an asymptotic field, in which difficulties caused by cancellation can be resolved. We show how to pass from a given asymptotic field to an elementary extension. The asymptotic expressions we derive will, in general, contain a finite number of arbitrary constants of integration. In addition, we provide different asymptotic expressions corresponding to different branches of algebraic functions. We do not determine the arbitrary constants or branches by specifying values at finite points.
Magnetic Reconnection Scenario of the Bastille Day 2000 Flare

On the basis of Yohkoh Hard X-Ray Telescope data and the magnetograms taken by the SOHO Michelson Doppler Imager and the Solar Magnetic Field Telescope at Huairou Solar Observing Station, we suggest an interpretation of the well-observed "Bastille Day 2000" flare. The large-scale structure and dynamics of the flare, as seen in hard X-rays, can be explained in terms of the three-dimensional reconnection at a separator in the corona. More specifically, we suggest that before occurrence of two-ribbon flares with significant decrease of a distance between the hard X-ray (HXR) footpoints, like the Bastille Day flare, the bases of magnetic field separatrices are moved by the large-scale photospheric flows of two types. First, the shear flows, which are parallel to the photospheric neutral line, increase the length of field lines in the corona and produce an excess of magnetic energy. Second, the converging flows, i.e., the flows directed to the neutral line, create the preflare current layers in the corona and provide an excess of energy sufficient to produce a large flare. During the flare, both excesses of magnetic energy are released completely or partially. In the Bastille Day flare, the model describes two kinds of apparent motions of the HXR kernels. One is an increase of a distance between the flare ribbons in which the HXR kernels appear. The effect results from fast reconnection in a coronal current layer. The second effect is a decrease of the distance between the kernels moving to each other as a result of relaxation of magnetic tensions generated by the photospheric shear flows.
Space-Time scales of internal waves

Abstract We have contrived a model E(αω) α μ−1ω−p+1(ω 2−ω i 2)−+ for the distribution of internal wave energy in horizontal wavenumber, frequency-space, with wavenumber α extending to some upper limit μ(ω) α ω r-1 (ω 2−ω i 2)½, and frequency ω extending from the inertial frequency ω i to the local Vaisala frequency n(y). The spectrum is portrayed as an equivalent continuum to which the modal structure (if it exists) is not vital. We assume horizontal isotropy, E(α, ω) = 2παE(α1, α2, ω), with α1, α2 designating components of α. Certain moments of E(α1, α2, ω) can be derived from observations. (i) Moored (or freely floating) devices measuring horizontal current u(t), vertical displacement η(t),…, yield the frequency spectra F (u,η,…)(ω) = ∫∫ (U 2, Z 2,…)E(α1, ∞2, ω) dα1 dα2, where U, Z,… are the appropriate wave functions. (ii) Similarly towed measurements give the wavenumber spectrum F (…)(α1) = ∫∫… dα2 dω. (iii) Moored measurements horizontally separated by X yield the coherence spectrum R(X, ω) which is .
History and Accomplishments of the North American Clinical Trials Network (NACTN) for Spinal Cord Injury, 2004 to 2022.

This is a historical account of the origin and accomplishments of the North American Clinical Trials Network (NACTN) for traumatic spinal cord injury (SCI), which was established in 2004 by Christopher Reeve and Dr. Robert Grossman. Christopher Reeve was an actor who became quadriplegic and started the Christopher and Dana Reeve Foundation, while Robert Grossman was a neurosurgeon experienced in neurotrauma and a university professor in Houston. NACTN has member investigators at university and military centers in North America and has contributed greatly to the improvement of care, primarily acute care, of patients sustaining traumatic SCI. Its accomplishments are a clinical registry database of more than 1000 acute SCI patients documenting the care pathways, including complications. NACTN determined the effectiveness of treatment, including pharmacotherapy and the role and timing of surgery, and barriers to early surgery with a focus on neurological recovery. NACTN has assessed the effectiveness of treatment, including pharmacotherapy and the role and timing of surgery, and has also identified barriers to early surgery. The principal focus has been on improving neurological recovery. It has trained many SCI practitioners and has collaborated with many other SCI networks and organizations internationally to promote the care of SCI patients.
Structured singular values with nondiagonal structures. II. Computation

In part I we introduced a robust stability measure-termed generalized structured singular value. In part II we address computational issues pertaining to this notion. Our main contribution is a computational method which would render the computation of the generalized structured singular value both more efficient and potentially more accurate. As in the computation of the usual structured singular value, the key in our method is to compute an upper norm bound scaled via a similarity transformation. It is shown that this bound is as tight as those obtained elsewhere and that it can be computed considerably more efficiently. Furthermore, it is shown that this bound is actually equal to the generalized structured singular value when the uncertainty has four blocks.
The network structure of the Special Interests Survey

Despite the prevalence of special interests (SIs) in autistic youth, research on SIs and how they are characterized is limited. Indeed, a significant challenge in identifying and classifying SIs lies in capturing the vast and diverse scope of potential interests in this population. The recently developed Special Interest Survey (SIS) is a caregiver‐report measure to improve SI characterization by capturing a broad range of past and current SIs. In the present study, we performed a network analysis of the SIS to examine relations between SIs and identify distinct interest clusters. We analyzed data from 1992 caregivers of autistic youths who completed the SIS. The network of SIs was densely interconnected, characterized by six communities of interests: Fact‐seeking, Engineering, Order‐seeking, Object Attachment, Entertainment, and Scholarly Pursuits. Findings suggest that the structure of the observed network is likely to generalize to similar samples. Of all the SIs and their respective communities, behaviors related to Fact‐seeking were identified as the most central, meaning that endorsement of these interests was most strongly related to co‐endorsement of other SIs. These findings lay the groundwork for future work on SIs, such as improved assessment techniques and linkage of SIs to a broad range of demographic variables, youth characteristics, and autism symptoms.
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications remains challenging. DL algorithms are often computationally expensive, power-hungry, and require large memory to process complex and iterative operations of millions of parameters. Hence, training and inference of DL models are typically performed on high-performance computing (HPC) clusters in the cloud. Data transmission to the cloud results in high latency, round-trip delay, security and privacy concerns, and the inability of real-time decisions. Thus, processing on edge devices can significantly reduce cloud transmission cost. Edge devices are end devices closest to the user, such as mobile phones, cyber–physical systems (CPSs), wearables, the Internet of Things (IoT), embedded and autonomous systems, and intelligent sensors. These devices have limited memory, computing resources, and power-handling capability. Therefore, optimization techniques at both the hardware and software levels have been developed to handle the DL deployment efficiently on the edge. Understanding the existing research, challenges, and opportunities is fundamental to leveraging the next generation of edge devices with artificial intelligence (AI) capability. Mainly, four research directions have been pursued for efficient DL inference on edge devices: 1) novel DL architecture and algorithm design; 2) optimization of existing DL methods; 3) development of algorithm–hardware codesign; and 4) efficient accelerator design for DL deployment. This article focuses on surveying each of the four research directions, providing a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference.
3D finite element analysis of rubber‐like materials at finite strains

The paper starts with a review of constitutive equations for rubber‐like materials, formulated in the invariants of the right Cauchy—Green deformation tensor. A general framework for the derivation of the stress tensor and the tangent moduli for invariant‐based models, for both the reference and the current configuration, is presented. The free energy of incompressible rubber‐like materials is extended to a compressible formulation by adding the volumetric part of the free energy. In order to overcome numerical problems encountered with displacement‐based finite element formulations for nearly incompressible materials, three‐dimensional finite elements, based on a penalty‐type formulation, are proposed. They are characterized by applying reduced integration to the volumetric parts of the tangent stiffness matrix and the pressure‐related parts of the internal force vector only. Moreover, hybrid finite elements are proposed. They are based on a three‐field variational principle, characterized by treating th.
A Survey on Remote Operation of Road Vehicles

In recent years, the use of remote operation has been proposed as a bridge towards driverless mobility by providing human assistance remotely when an automated driving system finds a situation that is ambiguous and requires input from a remote operator. The remote operation of road vehicles has also been proposed as a way to enable drivers to operate vehicles from safer and more comfortable locations. While commercial solutions for remote operation exist, remaining challenges are being tackled by the research community, who is continuously testing and validating the feasibility of deploying remote operation of road vehicles on public roads. These tests range from the technological scope to social aspects such as acceptability and usability that affect human performance. This survey presents a compilation of works that approach the remote operation of road vehicles. We start by describing the basic architecture of remote operation systems and classify their modes of operation depending on the level of human intervention. We use this classification to organize and present recent and relevant work on the field from industry and academia. Finally, we identify the challenges in the deployment of remote operation systems in the technological, regulatory, and commercial scopes.
OBSERVATIONS OF UMBRAL FLASHES AND RUNNING SUNSPOT WAVES WITH THE INTERFACE REGION IMAGING SPECTROGRAPH

We present simultaneous, high-resolution, multi-temperature observations of running waves and umbral flashes in a sunspot with the Interface Region Imaging Spectrograph. We analyze intensity variations in slit-jaw images to investigate the relationship between running waves in the 1400 Å (Si iv/transition region) passband and umbral flashes in the 2796 Å (Mg ii/chromosphere) passband. Using global wavelet analysis, we find that the dominant wave periods increase from approximately 150 s near the sunspot center to about 300 s in the penumbra in both passbands. This coincides extremely well with the radially increasing inclination of magnetic field lines observed with Solar Dynamics Observatory/Helioseismic and Magnetic Imager, suggesting that the waves are propagating vertically along the inclined field lines. Furthermore, apparent horizontal velocities for running waves decrease from about 12 km s−1 in the umbra to about 4 km s−1 in the penumbra which is predicted by the same inclined field geometry. Finally, we find that umbral flashes lead running waves in both the spatial and temporal domains. The former result is attributable to the inclined field geometry; however, the geometry does not predict the radially increasing time lag which is likely due to the opacity difference between the emission lines that dominate the two passbands. These results suggest that running waves and umbral flashes both originate from photospheric p-mode oscillations and are manifestations of upward-propagating slow magnetoacoustic modes at different altitudes and temperatures. The apparent trans-sunspot propagation of running waves is not real but rather an image of these upward-propagating modes traveling along field lines of radially increasing inclination.
A VANET Collision Warning System with Cloud Aided Pliable Q-Learning and Safety Message Dissemination

Ease of self-driving technological developments revives Vehicular Adhoc Networks (VANETs) and motivates the Intelligent Transportation System (ITS) to develop novel intelligent solutions to amplify the VANET safety and efficiency. Collision warning system plays a significant role in VANET due to the avoidance of fatalities in vehicle crashes. Different kinds of collision warning systems have been designed for diverse VANET scenarios. Among them, reinforcement-based machine learning algorithms receive much attention due to the dispensable of explicit modeling about the environment. However, it is a censorious task to retrieve the Q-learning parameters from the dynamic VANET environment effectively. To handle such issue and safer the VANET driving environment, this paper proposes a cloud aided pliable Q-Learning based Collision Warning Prediction and Safety message Dissemination (QCP-SD). The proposed QCP-SD integrates two mechanisms that are pliable Q-learning based collision prediction and Safety alert Message Dissemination. Firstly, the designing of pliable Q-learning parameters based on dynamic VANET factors with Q-learning enhances collision prediction accuracy. Further, it estimates the novel metric named as Collision Risk Factor (CRF) and minimizes the driving risks due to vehicle crashes. The execution of pliable Q-learning only at RSUs minimizes the vehicle burden and reduces the design complexity. Secondly, the QCP-SD sends alerts to the vehicles in the risky region through highly efficient next-hop disseminators selected based on a multi-attribute cost value. Moreover, the performance of QCP-SD is evaluated through Network Simulator (NS-2). The efficiency is analyzed using the performance metrics that are duplicate packet, latency, packet loss, packet delivery ratio, secondary collision, throughput, and overhead.
Institutionalizing sectoral business strategy through National Single Window to improve SMEs’ export performance

The Indonesia National Single Window (INSW) system is carried out with data governance policies to support the delivery and processing of data, information, and decisions in a single way, as in the research of Sobar, Deni, Riswandi, Hamidi, and Permadi (2021), and Riswanto (2021) to deal with the competition process and granting customs permits by the President’s mandate in Presidential Regulation No. 76 of 2014. This study aims to analyse how small and medium-sized enterprises (SMEs) in Central Java perform against the INSW in international trade. The research method used in this study is qualitative, namely, a research method that provides more analytical and subjective explanations. The results concluded that INSW uses an electronic portal to input documents. The use of electronic portals causes the import-export process to be faster and more efficient. However, the INSW system in its implementation is considered rudimentary because there are often problems in processes such as difficulty accessing the INSW portal or in other electronic service portals integrated with the INSW System. Therefore, intensive assistance and training are needed so that there is synergy in carrying out export activities to destination countries that have collaborated in Free Trade Asia with Indonesia.
Understanding what we know so far about young people's engagement with wellbeing apps. A scoping review and narrative synthesis

Background Increased levels of wellbeing contribute to people being more productive, resilient, physically healthy and showing lower levels of mental illness. Using mobile apps to increase wellbeing in young people is becoming the method of choice. This study sought to critically appraise the current evidence base with regards to young people's (16–24 years of age) engagement with wellbeing apps. Methods A systematic review of the literature and narrative synthesis was conducted to investigate users’ characteristics and other potential engagement elements. A total of 11,245 titles, 160 abstracts and 68 full-text articles published between 2002 and 2021 were screened, of which 22 studies were included. Results Main themes/findings indicated that a user's engagement with wellbeing apps was dependant on the presence of strong identity elements, including motivation, mood and values; design elements such as meaningful rewards, short duration of studies and seamless automatic delivery with low contact with researchers; and being innovative and contextualised. The majority of the studies did not report outcomes by social determinants such as ethnicity, education and others. Conclusion This research reflects on the need to consider participants’ individuality when designing app mediated wellbeing interventions.
Investigation of texture periodicity extraction

The structure extraction task is analyzed. The co-occurrence matrices (CMs) are the popular basis for this goal. We show that binary preparation of arbitrary texture preserves its structure. This transformation decreases the computation time of analysis and the required memory in dozens times. A number of features for detecting displacement vectors on binarized images are compared. We suggest to use CM elements jointly as the united feature for this goal. We have shown that it is a stable detector for noisy images and simpler than well- known (chi) 2 and (kappa) statistics.
Modelling acoustic parameters of prosody for read and acted‐speech synthesis

The prosody model is one of the most important parts of every speech synthesizer, influencing mainly its naturalness. The intonation contour and phoneme lengths (together with speech quality) bear a great deal of extralinguistic and paralinguistic information contained in the synthesized speech. The features reflecting personality, mood and emotions of the speaker are in strong interaction with those reflecting speech styles. Anyway the appropriate choose of prosody model and training material can make it possible to create special model for every speaking style. The paper presents our approach to modelling of acoustic parameters of prosody in two different speech styles in Slovak. Our model is based on Classification and regression trees (CARTs). It uses independent CART for phoneme lengths and three CARTs for fundamental frequency (F0) at the beginning, centre, and end of every syllable. Two hours of read speech were used for training a model of read speech. The recordings of a puppet player were used t.