Miracle-dz/UniFAR-FTUs huggingface dataset:
Politeness in the Use of Language in Social Media

The use of social media in communication changes the way people express the idea represented through their language. This study aims to seek for the form of communication patterns in social media and to reveal causes of specific pattern language usage found in media, especially in office domain. The study was conducted using descriptive qualitative by describing the results of observations, interviews, and documentation. The data were collected by non-participatory observation method and note taking technique. Based on the results of the analysis of the data, it can be concluded that the communication patterns are realized in the form of: (1) various forms of language which do not meet the standard language, (2) the use of other non-verbal representations, such as emoticon, and (3) ecrononciation, that is, writing the spoken language. The choice of utterances is affected by relative status, social distance and range of imposition. As communicating to others, most utterances represent the obedience to politeness maxim, namely be friendly (sumanak), be considerate (tepa slira), suitable topic to event (empan papan), and pleasant voice true and good temper (nuju prana).
ZXAD: High-volume Attack Mitigation for Tor

The Tor anonymity network is often abused by attackers to (anonymously) convey attack traffic. These attacks abuse Tor exit relays (i.e., the relays through which traffic exits Tor) by making it appear the attack originates there; as a result, many website operators indiscriminately block all Tor traffic (by blacklisting all exit IPs), reducing the usefulness of Tor. Recent research shows that majority of these attacks are ones that generate high traffic volume (e.g., Denial-of-Service attacks). This suggests that a simple solution such as throttling traffic flow at the Tor exits may permit early detection of these attacks, improve overall reputation of exits, and eventually prevent blanket blocking of Tor exits. However, naively monitoring and throttling traffic at the Tor exits can endanger the privacy of the network's users. This paper introduces ZXAD (pronounced "zed-zad"), a zero-knowledge based private Tor exit abuse detection system that permits identification of otherwise unlinkable connections that are part of a high-volume attack. ZXAD does not reveal any information, apart from the fact that some user is conveying a high volume of traffic through Tor. We formally prove the correctness and security of ZXAD. We also measure two proof-of-concept implementations of our zero-knowledge proofs and show that ZXAD operates with low bandwidth and processing overheads.
Infrared small target detection in compressive domain

A novel approach to detect an infrared small target in the compressive domain is presented. First, the original infrared image is projected on a sensing matrix to obtain the measurement vector. Then the target and the background are recovered simultaneously from the measurements based on low-rank and sparse matrix decomposition. Experimental results indicate that the proposed method not only works well under different complex backgrounds with less data storage, but also outperforms some existing methods in both subjective and objective qualities.
Combining serial and parallel decoding for turbo codes

Reducing the decoding latency of the turbo codes is important to real-time applications. Conventionally, the decoding of the turbo codes (TC) runs in serial fashion, which means only one of the constituent soft decoders runs at a time. Parallel decoding (PD) refers to running the soft decoders in parallel. Although it delivers the output faster (compared to the serial decoding (SD)), it affects the bit- and frame-error rates. This paper proposes a decoding procedure that combines both PD and SD. It bridges the two decoding modes to determine the best combination scheme to achieve the required level of performance at an acceptable decoding latency. Presented results show how this procedure can mitigate the performance degradation at a slight increase in the decoding latency.
Artificial Intelligence and Signal Decomposition Approach Applied to Retail Sales Forecasting

Sales forecasting is essential for decision-making and are crucial in many areas of a firm, such as planning and scheduling, resource management, marketing, logistics, and supply chain. Due to the fluctuations in retail sales, prediction with high accuracy is a challenging task. In this context, this study proposes a framework that combines ensemble empirical mode decomposition (EEMD) based on artificial intelligence models to forecast the retail sales of a Rossmann Store, using a multi-step-ahead forecasting strategy, in the task of time series forecasting with one, seven, and fourteen-days-ahead. The forecasting models of the retail sales time series are Bayesian Regularization of Artificial Neural Networks, Cubist Regression, and Support Vector Regression. The performance of the proposed forecasting models were evaluated by using two performance metrics: mean absolute percentage error and root mean squared percentage error. The EEMD models outperform the single models in all forecasting horizons, with a performance improvement that ranges 1.30% – 76.25%. Indeed, EEMD models are efficient and accurate models for retail sales forecasting.
Resolution improvement technique for two-dimensional detectors

It is presented a technique for enhancing the resolution of a 2D detector by meaning of multiple acquisitions and subsequent software processing of the acquired images, without modifying the optical configuration of the instrument nor the characteristics of the detector. The results of the application of the technique and the numerical algorithm are presented and discussed in terms of potentialities and limitations due to the intrinsic spectral broadening of the signals and to other sources of error, as uncertainty in the sub-pixel displacements or variations in intensity of the source. To check the reliability of the theoretical analysis, various software simulations have been performed, and one of these is presented in detail. Finally, some experimental results are presented, using a photon- counting multianode microchannel plate array detector system in the near ultraviolet region, with pixel size of 25 micrometers 2. A toroidal mirror focuses on the detector the image of a target, mounted on two micrometric translators, which allow the 2D displacements. Images of two fine wire mesh grids are presented, with period respectively 100 micrometers and 50 micrometers , and wire thickness of 13 micrometers . The reconstructed images are good, showing the asymmetrical profiles of the grids and a very low distortion.
Applying UTAUT and Fuzzy Dematel Methods: A New Legal Aid Administration System

A hybrid UTAUT and Fuzzy Dematel Method is proposed in assessing the perception of accepting the new electronic Legal Aid Administration (eLAA) system by employees of Legal Aid South Africa. Information technology acceptance research has in many respects provided insight to decision-making on reviewing user acceptance. Legal Aid SA’s employees’ perception measured by their response of assessment is useful information on users’ attitude of accepting the eLAA system. Therefore, this study aims to measure the main determinants of intention and usage of the new system by users using the Unified Theory of Acceptance and Use of Technology (UTAUT). Further, we identify the cause-and-effect factors of acceptance and suggest the importance of factors using the Fuzzy Decision-Making Trial and Evaluation Laboratory (Dematel) method. The findings of this study suggest that the UTAUT dimensions of Performance Expectancy has high direct influence on acceptance of the eLAA system by Legal Aid SA employees than other variances. Alternatively, Effort Expectancy (EE) dimension is in the overall having the least power of direct influence on using the eLAA system. Notably, both the Social Influence and Facilitating Conditions have low direct influence in predicting the attitude of using the eLAA system. The findings of this study are useful in assisting the management of Legal Aid SA to gain insight into preferred ways to introduce automated systems that are perceived useful and acceptable amongst employees.
A novel high resolution wide swath SAR based on waveform design

Spaceborne Synthetic Aperture Radar (SAR), with "all weather", day or night imaging capabilities, has been playing an important role in the domination of Earth observation. Spaceborne high-resolution wide-swath SAR (HRWS-SAR) can quickly obtain wide range of the earth’s surface information, which is of great significance to Earth mapping, geological exploration, vegetation and biomass estimates, marine monitoring, target search, disaster relief, etc. As a result, spaceborne HRWS-SAR has been gaining more and more attention. However, considering the restrictions on pulse repetition frequency (PRF) and power-aperture product, space-based SAR imaging cannot achieve high resolution and wide swath at the same time. Currently existing solutions mainly focus on the antenna system hardware devices, such as MIMO, DBF; other signal-processing-bias solutions, such as Mosaic imaging technology, have higher requirements of the antenna pointing or beam control. These methods adopt more antenna elements or complex beam control method, which greatly increased the demand for hardware performance, and the signal processing method become more complicated as well. In order to relieve the pressure on the system hardware devices, this paper presents a new orthogonal coded waveform method based on the theory of communication. By using this method, the LFM signal is coded by the orthogonal codes to make the inter-pulse waveform irrelevant, which ensures the azimuth sampling rate as well as a wide swath. Theoretically, this method can alleviate the contradiction between PRF and high resolution wide swath imaging.
Lessons Learned: Beta-Testing the Digital Health Checklist for Researchers Prompts a Call to Action by Behavioral Scientists

Digital technologies offer unique opportunities for health research. For example, Twitter posts can support public health surveillance to identify outbreaks (eg, influenza and COVID-19), and a wearable fitness tracker can provide real-time data collection to assess the effectiveness of a behavior change intervention. With these opportunities, it is necessary to consider the potential risks and benefits to research participants when using digital tools or strategies. Researchers need to be involved in the risk assessment process, as many tools in the marketplace (eg, wellness apps, fitness sensors) are underregulated. However, there is little guidance to assist researchers and institutional review boards in their evaluation of digital tools for research purposes. To address this gap, the Digital Health Checklist for Researchers (DHC-R) was developed as a decision support tool. A participatory research approach involving a group of behavioral scientists was used to inform DHC-R development. Scientists beta-tested the checklist by retrospectively evaluating the technologies they had chosen for use in their research. This paper describes the lessons learned because of their involvement in the beta-testing process and concludes with recommendations for how the DHC-R could be useful for a variety of digital health stakeholders. Recommendations focus on future research and policy development to support research ethics, including the development of best practices to advance safe and responsible digital health research.
Multiplex genomic recording of enhancer and signal transduction activity in mammalian cells

Measurements of gene expression and signal transduction activity are conventionally performed with methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm, termed ENGRAM (ENhancer-driven Genomic Recording of transcriptional Activity in Multiplex), in which the activity and dynamics of multiple transcriptional reporters are stably recorded to DNA. ENGRAM is based on the prime editing-mediated insertion of signal- or enhancer-specific barcodes to a genomically encoded recording unit. We show how this strategy can be used to concurrently genomically record the relative activity of at least hundreds of enhancers with high fidelity, sensitivity and reproducibility. Leveraging synthetic enhancers that are responsive to specific signal transduction pathways, we further demonstrate time- and concentration-dependent genomic recording of Wnt, NF-κB, and Tet-On activity. Finally, by coupling ENGRAM to sequential genome editing, we show how serially occurring molecular events can potentially be ordered. Looking forward, we envision that multiplex, ENGRAM-based recording of the strength, duration and order of enhancer and signal transduction activities has broad potential for application in functional genomics, developmental biology and neuroscience.
The Impact of Suspension Stiffness Parameters on the Curve through Performance of Straddle Type Monorail Vehicle Based on Sensitivity Analysis

The curve through performance of straddle type monorail vehicle include performance-oriented, preventing derailment stability, preventing vehicle overturning stability, tire wear performance, which are evaluated by oriented torque, the minimum vertical force of horizontal wheels, vehicle body roll angle, overturning coefficient, the sum of tire forces scalar. With the virtual simulation model in ADAMS, this paper analyzes the impact of the monorail vehicle suspension stiffness parameters on the evaluation indexes by sensitivity analysis.
The corporate university model for continuous learning, training and development

This paper discusses the emerging model of “corporate university” (CU) for continuous learning in the corporate world. With specially tailored training, CUs aim at improving the productivity and skills of employees using the latest advances in information and telecommunication technology. Training in this model particularly is more responsive to corporate needs than other learning intervention methods and adds value to the company business goals by helping recruit and retain talent. Their special focus is on disseminating common culture and driving changes in the entire organization. The article presents, briefly, the main characteristics of corporate universities and three examples. The article suggests that the CU model is sustainable to suit continuous employee learning and skill development and highlights the new approach of “active learning” that is increasingly being adopted as the future tool for training and development.
SINS attitude algorithm based on moving-window overdetermined polynomial fitting of gyro outputs

The attitude algorithm is the most important part of the whole strapdown inertial navigation processing. It calculates the attitude of certain parameterization by integrating the gyro outputs or measurements in a specifically tailored way according to the attitude kinematic differential equation. The measurements or some angular velocity models obtained by fitting these measurements are often assumed free of errors in order to assess the numerical errors only. However, the gyro outputs and hence the models from them are by no means free of measurement errors. It is more often than not that the measurement errors dominate the numerical ones in practice. In this study, with coping with the measurement errors as the focus, we aim to improve the angular velocity model which is used as input in an attitude integration algorithm. This is achieved by exploiting the potential of overdetermined least-squares polynomial fitting. In order to avoid reducing the update rate by incorporating more measurements, the moving window trick is employed to re-use measurements in the previous update interval. The conventional attitude algorithm with second-order approximation in solving the differential equation of the equivalent rotation vector is employed as an example; however, the proposed method can be readily applied to other parameterizations such as direction cosine matrix, quaternion or Rodrigues parameters, and other high order approximations in solving the differential equation widely studied recently.
Influence of Gain Dispersion on the Propagation Characteristics of Ultrashort Optical Pulses in Distributed Fiber Amplifiers

The physical model for the optical pulse propagating in the amplifier has been established. Within the normal dispersion region of the fiber, the evolution of the optical pulse in the amplifier has been simulated numerically, and the influence of gain dispersion on the propagation characteristics of the pulse has been discussed emphatically. The results show that, during the propagation of the ultrashort optical pulse in a distributed fiber amplifier, group velocity dispersion and Kerr nonlinear effect will affect the pulse profile and spectrum. For the ultrashort optical pulse with a wideband spectrum, the influence of gain spectrum on the propagation characteristics of the optical pulse, in essence, is that of gain dispersion, which is equivalent to a loss mechanism and will generate gain narrowing effect, so it should be considered in the design of an amplifier system.
Acid Sphingomyelinase Controls Early Phases of Skeletal Muscle Regeneration by Shaping the Macrophage Phenotype

Skeletal muscle regeneration is a complex process involving crosstalk between immune cells and myogenic precursor cells, i.e., satellite cells. In this scenario, macrophage recruitment in damaged muscles is a mandatory step for tissue repair since pro-inflammatory M1 macrophages promote the activation of satellite cells, stimulating their proliferation and then, after switching into anti-inflammatory M2 macrophages, they prompt satellite cells’ differentiation into myotubes and resolve inflammation. Here, we show that acid sphingomyelinase (ASMase), a key enzyme in sphingolipid metabolism, is activated after skeletal muscle injury induced in vivo by the injection of cardiotoxin. ASMase ablation shortens the early phases of skeletal muscle regeneration without affecting satellite cell behavior. Of interest, ASMase regulates the balance between M1 and M2 macrophages in the injured muscles so that the absence of the enzyme reduces inflammation. The analysis of macrophage populations indicates that these events depend on the altered polarization of M1 macrophages towards an M2 phenotype. Our results unravel a novel role of ASMase in regulating immune response during muscle regeneration/repair and suggest ASMase as a supplemental therapeutic target in conditions of redundant inflammation that impairs muscle recovery.
Selecting ANN structures to find transmission faults

Programming models based on artificial neural networks (ANN) have seen increased usage. ANNs are used in various fields (industry, medicine, finance, or new technology, among others). There is a wide range of possible power system applications of neural networks in operation and control processes, including stability assessment, security monitoring, load forecasting, state estimation, load flow analysis, contingency analysis, emergency control actions, HVDC system design, etc. This article features an automatic system that selects the most adequate ANN structure to solve any type of problem. The ANN Automatic Selection System (SARENEUR) was implemented in a specific case in order to obtain a neural network structure that shows better results in fault location within a two-terminal transmission line. The fault location is obtained according to the values of steady-state voltages and currents measured at one end.
Subsurface temperature from seismic reflection data: application to the post break up sequence offshore Namibia

Accurate estimations of present-day subsurface temperatures are of critical importance to the energy industry, in particular with regards to geothermal energy and petroleum exploration. This paper uses seismic reflection observations of bottom-simulating reflections and subsurface velocities coupled with an empirical velocity to thermal conductivity transform to estimate subsurface temperature in a process dubbed reflection seismic thermometry. The case study is a frontier passive margin extending from the shelf edge to deep water in the central Lüderitz Basin, offshore Namibia. The bottom simulating reflector is used to derive surface heat flow. The thermal conductivity model was applied to seismic processing velocities to determine the subsurface thermal conductivity. Knowledge of surface heat flow and thermal conductivity structure allowed us to estimate subsurface temperatures across the study area. The results suggest the Lüderitz Basin has a working hydrocarbon system with the inferred Aptian Kudu source interval within the gas generation window.
Primary electric propulsion for future space missions

The paper presents data and a methodology to allow preliminary definition of electric propulsion systems. The elements comprising the thrust system are described parametrically. As an example, thruster performance is presented as a function of specific impulse and propellant type. Two power management and control (PMAC) approaches are considered to illustrate the use of the methodology. Power source characteristics are disregarded in the system description. One PMAC concept assumes a dc power input to the thrust system and all thruster power conditioned in a conventional manner; the other PMAC approach assumes an ac power source.
Importance of P-Series Fuels for Flexible-Fuel Vehicles (FFVs) and Alternative Fuels

As engine fuels, the most popular alternative fuels are bioetanol, biodiesel, and hydrogen. Recently, in addition to these, there are intensive researches on methyl-, and ethylalcohols, natural gas, liquefied petroleum gas, P-series, electricity, and solar fuels. Alternative fuels for diesel engines are becoming increasingly important due to diminishing petroleum reserves and the environmental consequences of exhaust gases from petroleum-fueled engines. One of the advantages of P-series is that they are very easy to use. There is no need for any special fuel management because gasoline and P-series can be freely intermixed in any proportion with fuel that is already in the vehicle's fuel tank. So, even if P-series is not available at a particular location, simply fill up with gasoline. These fuels are inexpensive fuels generated by municipal and agricultural wastes. The National Renewal Energy Laboratory (NREL) showed that P-series would be 96% derived from domestic resources and reduce petroleum use by 80% as compared to gasoline. Use of P-series fuels also greatly reduces toxic emissions. P-fuels are economically competitive with gasoline. As of May 2003, the projected retail price for P-series, including all taxes, is $1.49 per gallon, about the same as mid-grade gasoline in a $/mile calculation. There are 3 million cars on the road today that could run on P-series fuels.