Miracle-dz/UniFAR-FTUs huggingface dataset:
Does associativity affect likelihood Peruvian small-scale farms internationalization?

in recent years the internationalization process in the Peruvian agricultural sector has been experiencing significant growth because of the dynamism of the agro-export sector. Nonetheless, the high degree of farm atomization and farmer's dispersion is one of the remaining structural issues for better productivity and competitiveness rates in the agricultural sector. Thereby increasing the benefit rates of smallholder farmers because they are part of the global agrifood system, is still a remaining challenge. This study's aim was to explore the predictive role of characteristics in smallholder to belong to a farmer organization, as conditions that make it possible to predict smallholder farmers' access to the foreign market as a benefit of associativity. This research use data from the Peruvian Agricultural National Survey (ENA) collected in 2017. Through a logistic regression approach, farm conditions like territory, farmer organization type, farmer organization age, and farm core activity (crop and/or livestock), were tested as factors to increase the farms' likelihood to get access to foreign markets.
An Approach for Discovering Affiliation Network from XES Formatted Workflow Log

For more than a decade, process-aware knowledge discovery has received the attention of many scientists and research groups. Many algorithms, techniques, and technologies have been applied to exploit information recorded in the history of business process systems. By elucidating the information that comes from the event log, we better understand what happened in the system. In this paper, we propose an approach to discover affiliation network from the workflow enactment event log recorded in XES format. More precisely, we describe an algorithm to exploit the relationship between activities and performers in the system, represent it under the affiliation network model and conduct it on the Conformance Checking Challenge 2019 data set. By discovering the affiliation network from event log, not only we understand more about the relationship between performers and activities, but also we have an overall view of the role of performers with the operation of the system. The results of this study will contribute to the evaluation and improvement of the performance in information systems.
Why Simple Models Perform Well in Predicting Popularity for Caching?

Predicting file popularity is an important task for proactive caching, which has been shown a promising way to support the explosive increase of data traffic. Both linear and non-linear predictors have been proposed in literature to predict file popularity, and shallow neural networks have been shown to achieve good performance for caching. In this paper, we strive to explain why simple models perform well in predicting dynamic popularity with the historical number of requests. We consider linear regression, deep neural networks, random forest, support vector regression, and a persistence model for predicting popularity. We employ MovieLens and Youku datasets for analyzing the time-varying pattern of popularity and for evaluating the caching performance. We show that the cache hit ratios achieved by the caching policy with predicted popularity using linear models are close to that using non-linear models. We interpret the observation by first proving that deterministic popularity with typical profiles can be predicted with linear models and then showing that majority of the popular files are with these profiles in the real dataset.
Mobile robot tele-operation support using gaze intention estimator with saliency map and outlier test

Narrow angle of view of the camera mounted on the mobile remote-controlled robots makes the operation difficult. In order to solve this problem, a panning support system utilizing the followings is presented in this paper: estimation of operator’s attention movement and estimation of gaze point candidate. In this system, the gaze movement is detected using a Kullback-Leibler outlier test, and the gaze points are estimated utilizing a saliency map. This paper introduces the configuration of this support system and the outlier test method using Kullback-Leibler, and the generation of the saliency maps. The primely measurement / analysis using a mobile robot operation experiment in virtual space was performed to confirm the presented algorithms and methods.
Research on Lane Occupancy Rate Forecasting Based on the Capsule Network

This paper proposes a hybrid lane occupancy rate prediction model called 2LayersCapsNet, which combines the improved capsule network and convolutional neural networks (CNNs). The model uses CNNs to mine the spatial-temporal correlation characteristics of the lane occupancy rate and then uses an improved capsule network to mine the interrelationships in traffic data measured by sensors in continuous time intervals to predict the lane occupancy rate. The model can solve the problem of CNNs losing important spatiotemporal information caused by the maximum pooling operation and can obtain better prediction results. To verify the efficiency of the 2LayersCapsNet model, the model is compared with the capsule network model (CapsNet), convolutional neural networks model (CNNs), recurrent neural networks model (RNNs), long short-term memory model (LSTM) and stacked autoencoders model (SAEs) with similar network structures and parameter settings on the PEMS-SF data set. The experimental results indicate that the 2LayersCapsNet model can obtain a prediction model faster than the CapsNet model and that 2LayersCapsNet performs better than the CNNs, RNNs, LSTM and SAEs with respect to three evaluation metrics, namely, MAPE, MAE and RMSE, on four prediction tasks.
Calibration of Laser Optics for Sensor Guided Remote Cutting

In the last several years, laser based material processing has established itself in many industrial branches as an economical manufacturing process. Laser remote cutting in particular is widespread in the automotive sector. In order to take full advantage of laser remote cutting, it is necessary to position the laser beam precisely on the workpiece using a handling kinematic system. Therefore, this work focuses on the combination of standard industrial robots with laser scanner optics. To address this configuration, this paper proposes a method for the calibration of the parameters, which describe the precise position of the 3D laser scanner optics in respect to the robot. Moreover, the robot introduces a new source of deviation, which is then compensated by using an external measuring system to guide the scanner optics during processing.
Using Natural Language Processing to Accelerate Deep Analysis of Open-Ended Survey Data

In this poster session, we share our work exploring the use of natural language processing (NLP) to assist in the thematic analysis of qualitative data. With the affordances currently provided by artificial intelligence and machine learning to process textual data for meaning, our interdisciplinary team of computer and social science researchers is using a data set of open-ended text responses to test procedures for creating thematic categories to organize participant responses by content. By comparing parallel analyses of our text corpus using traditional and NLP thematic-analysis techniques, we identify key areas of congruity and incongruity between human analysts and NLP algorithms. While the areas of congruity point to the potential usefulness of NLP systems in qualitative research, the areas of incongruity suggest the need for a “human in the loop” NLP platform which iteratively refines the content analysis outputs based on feedback from an expert researcher until a satisfactorily coherent thematic description is achieved.
Novel Dual-Band Equal-Cell Doherty Amplifier Design With Extended Power Back-Off Range

In this article, for the first time, a new and generalized design methodology for dual-band Doherty power amplifier (DPA) is presented. Owing to the adoption of equal-cell architecture and extended power output back-off (OBO > 6 dB) operation, the conventional dual-band impedance-transformer approach cannot be applied. Because of this, a novel output combiner is proposed for the provision of load modulation at two widely spaced frequency bands. For verification, a 9-dB-OBO DPA with 1.4- and 3.5-GHz operating frequencies is designed and fabricated. Simulation and measurement results indicate excellent Doherty efficiency behavior with back-off (9 dB) efficiency of over 55% and saturation efficiency of over 68% at both frequencies. Under 9-dB-peak-to-average power ratio (PAPR) WCDMA signal excitation, average efficiency of 52.1% and 51.3% is achieved at 1.4 and 3.5 GHz, respectively.
Quality & Reliability Assessment of USB-PLL Clock Failure in Silicon Products

Quality and reliability are one of the crucial test features in post silicon validation which ensures the performance of flawless silicon production. This paper addresses the issue related to random clock valid failure of USB-PLL during the series of reliability testing on 28nm SoC product. However, in existence of such an unpredictable issue, silicon production cannot happen; though during the design verification and functional validation, the USB PLL clock valid was working as per design. To avoid production discontinuation, a permanent solution is need of the hour. So, this issue has been replicated on analog bench validation for further debugging and root-causing. During the debugging, PHY's clock validity issue was isolated and PLL clock was not obtained at digital logic of the USB. Thus, we propose the immediate solution for this product-level reliability problem by adding a reset sequence through a software setting. In addition to above, designers have been requested for thorough review of robustness of the oscillator concept to optimize this issue from designer perspective.
Optimal Motion Trajectory Generation for Automotive Windshield Wiper Systems Using the Response Surface Method

A wiper systems is one of important safety related automotive parts to remove rain and snow from a vehicle’s windshield. Although there are several criteria to evaluate the wiper motion, over-run, which is the excessive amount of motion of a wiper system, may cause significant damage to itself and generate noise in particular by colliding with a pillar and a body of a vehicle. This study focuses on over-run reduction by optimizing the motion trajectory. Frist, this paper presents a new representation of wiper motion trajectory, by which the peak velocity position and the velocity reduction ratio around the reversal position of motion can be explicitly adjusted. Next, these two items are optimized by using the surface response method. Simulation and experimental results demonstrate the effectiveness of the proposed approach.
Development of a Glass Technology Based 79 GHz MIMO Radar Front-End Module for Autonomous Driving

In this paper, a holistic approach, the M3-approach (Methodologies, Models, Measures), is applied for the efficient and accurate design of a 79 GHz scalable MIMO radar frontend for autonomous driving application. First, based on the targeted application and the international regulatory requirements, we derive the component and system specifications of a scalable MIMO hardware architecture. Next, a concept for the low-cost realization of the MIMO radar front-end module by means of a commercial glass interposer technology is derived. The proposed module is modeled and its performance investigated by means of 3D full-wave simulations, both at component and packaging level. Test samples are fabricated and measured to verify the results of the full-wave simulations. Excellent correlation between measurements and simulations is achieved. Finally, the verified models are applied to optimize the RF performance of the MIMO radar components and front-end, without re-design iteration.
An Overloaded SC-CP IoT Signal Detection Method via Sparse Complex Discrete-Valued Vector Reconstruction

The paper proposes low-latency signal detection methods for overloaded MU-MIMO (Multi-User Multi-Input Multi-Output) SC-CP (Single Carrier block transmission with Cyclic Prefix) using convex optimization approach for uplink IoT (Internet of Things) environments where a lot of IoT terminals are served by a base station having less number of antennas than that of IoT terminals. The proposed method detects overloaded IoT signals via convex optimization approach named sum of complex sparse regularizers (SCSR) taking advantage of both the discreteness and the sparsity of the SC-CP IoT signal. Simulation results demonstrate the validity of the proposed method.
A Finite-difference Time Domain Method for Analysis in Optical Waveguides: The Time Stability Determination

This paper proposes a method how to determine the critical time step for use in Finite-Difference Time Domain (FDTD) analysis. Typically, the method is requires an appropriate time step in the calculation. The method is based on the criterion of stability increasing time must less than the time for the increasing propagation distance used in the calculation. For the large time step, the calculation would be diverged whereas the small time step would consumed huge execution time for getting a result. Therefore, we propose a method to determine the value of the critical time step of stability in the FDTD calculation for the optical slab waveguide. We investigate the maximum time step to provide the minimum time expensed in the calculation with maintaining the stability in the beam propagation. A ratio of the transversal step to the longitudinal step or vice versa, as greater than unity, F, and the fraction, A, of the stability time step are established for this method. Both parameters would simplified the derivatives terms of the electric and magnetic fields in the Maxwell’s Equations. The execution time would reasonably decreased. Alternatively, the relationship between the A and the fraction number F was calculated and represented by A = 0.003F4 − 0.135F3 + 2.24F2 − 17.73F + 66.78. It is found that the maximum time step is limited at 50% of the propagation time through the guide. The critical time step, which is used in the FDTD calculation, can be determined by the fractional critical time step A. With the guide length/width ratio > 10, the fractional critical time step should be not more than 10%. To implement the graph, we chose F of 4, corresponding to A of 19%. So, the simulation does diverge when A of 20%. The simulated results at the critical A and the small A of 9.5% have the same beam patterns.
Real-time data-driven estimation of radar cross-section of vehicles

Radar technology is one of key elements integrated in autonomous driving systems and advanced driver assistance systems (ADAS). Development of ADAS requires extensive validation of the involved radar systems, and real test driving for the validation can be costly. Although promising as a replacement for real test driving, virtual test driving to simulate radar systems in detail using physics-based electromagnetic simulation techniques is time consuming. This paper describes a data-driven approach to reduce the computation time for such simulations. As an initial attempt to introduce a machine learning model to simulate electromagnetic reflection property of vehicles based on their shapes, convolutional neural networks (CNN) whose input and output are the vehicle shape and its radar cross-section (RCS), respectively, are trained. The correlation coefficient between the estimated and ground truth RCSs can be as high as around 0.8 while the computation speed is significantly increased with a speed-up of approximately 50 times with respect to the ray-tracing method.
Application of Deep Learning to IMU sensor motion

Deep learning, a sub of machine learning, is a powerful tool for pattern classification but is currently underutilized for IMU motion classification. The digit classification task using the MNIST dataset is one of the most conceptually simple machine learning tutorials and serves as a starting point for other classification tasks. In this paper, we propose to apply deep learning to a set of IMU (inertial measurement unit) sensor-based characters to test how well deep learning works to classify a set of written numbers similar to the open-source MNIST database. Our experiment demonstrates that a deep learning model can correctly classify IMU motion sensor readings tracing out digits in space. These results successfully prepare a deep learning framework for more complex IMU motion classification tasks, such as automatic configuration of grasps and control in biomechatronic prosthetics.
Error Analysis and Calibration for a Novel Pipe Profiling Tool

Integrity of industrial pipework is ensured through routine inspection. Internal visual inspection tools are capable of characterising degradation in the form of corrosion, pitting, erosion and cracking. The accuracy of such inspection systems has a direct impact on decisions regarding the remaining lifetime of the asset. By minimising error margins, the asset may be operated with confidence for longer, with less uncertainty. This paper considers a probe system consisting of a laser profiler and camera that produces a textured 3D model of the internals of 2 – 6 inch pipework. The accuracy of the system is defined by the ability to extract laser projections from an image as it travels down the pipe, to accurately reconstruct these projections into 3D and to estimate the probe trajectory as it travels through the pipe. This paper presents an error model of the laser profiler. It then presents a novel calibration routine to reduce the error caused by misalignment and tolerances during fabrication of the system. A key advantage of the proposed calibration technique over alternatives is that we can calibrate for errors without manually adjusting the probe, which enables fabrication of a smaller more robust measurement system. In lab-based trials our calibration technique reduced peak sizing errors from 2.7 mm to 0.14 mm in 120 mm diameter pipes.
Dual Polarized Ultrawideband Coincident Phase Center TCDA with 15:1 Bandwidth

An ultrawideband (UWB) Tightly Coupled Dipole Array (TCDA) is demonstrated with 15:1 bandwidth and operates from 130 MHz – 2 GHz. In this paper we present the design, fabrication and demonstration of a dual-linear polarized array with coincident phase centers and capable of conical scanning to 60 degrees. The TCDA employs multilayer frequency selective surfaces, a spatially varying metamaterial wide-angle impedance matching superstrate as well as a ferrite substrate. A 225 element (15×15) prototype is assembled using modular subarrays. Embedded element patterns are verified experimentally and compared to numerical simulation. Unlike other UWB TCDA designs in literature the aperture has half wavelength lattice at the highest operating frequency, and the matching networks and power combiners do not extend below the ground plane maintaining the aperture low-profile thickness.
Force-feedback in Virtual Reality through PGM-based ForceHand glove

This paper introduces a new method of rendering force-feedback at the wrist using low-pressure type pneumatic artificial muscles called pneumatic gel muscles (PGMs) in virtual environments. Four types of force-feedback are designed to realize the physical properties of soft, hard, repulsive, and pass-through objects. Two types of actuation methods are used to implement the feedback. The first method uses binary actuation, whereas the second method uses PWM-based control of the air pressure fed to the PGMs. The force due to the stiffness property of the PGM was modelled using the spring model, and this model was used to implement the second method of PWM-based actuation. A user study was conducted to compare both the actuation methods, and the majority of subjective feedback from the participants was in favor of the PWM-based actuation.
Beam Management Solution Using Q-Learning Framework

The beam management is a procedure that properly selects the beams to overcome strong path loss attenuation. This procedure is specially important in 5G-NR (new radio) deployments that operate in millimeter waves frequencies. We propose a novel beam tracking solution that is based on a reinforcement learning framework. More specifically, using the Q-learning algorithm, the user equipments learn over time the best set of beams to maximize their own signal-to-noise to interference ratio. Our framework takes into account reference signals in the 5G technical specification and propose a measurement protocol to implement the Q-learning. The proposed method shows better spectral efficiency than the beam sweeping technique for the multi-user MIMO case.
Is average RMSE appropriate for evaluating acoustic-to-articulatory inversion?

Acoustic-to-articulatory inversion has potential application in number of fields. For decades, average root mean square error and Pearson correlation coefficient are the most prevalent quantities adopted to evaluate the performance of acoustic-to-articulatory inversion. Various inversion methods have been developed to less the average root mean square error, but very few studies explored whether the average root mean square error is appropriate for evaluating and comparing the performance of different inversion methods. In this study, we attempt to tackle this issue by comparing not only the average root mean square error but also channel root mean square error of each articulatory channel, and the root mean square error of the critical and non-critical portions of each articulatory channel for methods within and between different groups. It is found that: i) the root mean square error of each articulatory channel, and the root mean square error of the critical and non-critical portions of each articulatory channel decrease while the average root mean square error decrease if the AAI methods belong to the same group; ii) exceptions are found if the inversion methods belong to different categories; iii) the average root mean square error is dominated by that of non-critical portions of articulatory channels. This suggests that new methods, which pay more attention to the performance of acoustic-to-articulatory inversion on critical articulators and facilitate the comparison of performance of methods belonging to different categories, should be developed in the future.
Temporal Impulse of Traffic Accidents in South Korea

With the emergence of urban computing technology, the development of smart cities has gained much attention as a means to improve citizens’ quality of life. As traffic accidents constitute a major problem that affects the quality of life, an effective solution to address this problem can significantly increase the level of intelligence of smart cities. This paper presents the development of a mathematical model for accurate analysis of big data to promote the effectiveness of policy decisions, thereby largely advancing the intelligent transportation systems (ITS) of smart cities. Temporal impulse was designed as a novel and measurable quantity to analyze traffic accidents by identifying the hidden patterns, such as varying causes and diverging impacts of traffic accidents. Based on the big data produced by the South Korean National Police Agency, we analyzed traffic accidents over three years by applying the temporal impulse. The research results suggested that the temporal impulse not only helped in identifying the varying influence of weather and driver conditions but also facilitated the establishment of sophisticated policies in the implementation of smart cities with the use of urban computing technology. As presented in the section VII, our simulation outputs indicated that our temporal model was predictive within the parameter space comprising driver’s dynamic behaviors, day of the week, and environmental factors including weather, road surface condition, and road type.