Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Resolution Driving Scene Synthesis Using Stacked Conditional Gans and Spectral Normalization

Large-scale dataset plays a key role in the driving scene understanding for deep learning based-autonomous driving tasks. Due to the fact that the annotation for a large number of images is extremely labor-intensive and time-consuming, many researchers turn to using image-synthesis techniques for automatic construction of training data. However, traditional methods often have difficulties in producing high-definition driving scene images. To tackle this problem, in this paper, we propose a novel deep model – hdCGAN – for high-definition image-to-image translation. The hdCGAN is built on a conditional GAN in combination with a spectral normalization. Moreover, we improve the hdCGAN by using a stacked network architecture and the enhanced model is called stack-hdCGAN. With the guidance of multi-scale discriminators and the constraint of spectral normalization in the training procedure, the learned models can generate high-resolution and high-quality driving scene images from corresponding semantic segmentation maps. Quantitative and qualitative evaluations on the Cityscapes dataset demonstrate the effectiveness of the proposed models.
Similarity Test for Privacy-Preserving Medical Data Sharing Based on NTRU Encryption

Recently, how to share the encrypted data efficiently from the cloud service provider becomes a hot topic, and massive research works have studied on Equality Test for encryption files of secure cloud storage. The basic idea of sharing information is to find the encrypted target information. Considering the scheme of identity-based encryption with equal test (IBEET) is defined as a feasible scheme, but it can only search for ciphertext composed of identical plaintext. However, the above technologies have seriously affected information sharing, because it greatly limits the search scope. Especially in some special areas, such as medical industries, doctors need to share similar medical information for auxiliary treatment and autonomous learning to improve the personal business ability. We propose the similarity test for sharing the privacy-preserving medical information in cloud environment. Our scheme proposal focuses on the Locality-Sensitive Hashing function to generate the index for protecting the upload information. In addition, our scheme utilizes the identity based on NTRU encryption to achieve resistance to quantum attacks.
A Data-Driven Framework for Appearance Editing of Measured Materials

In this paper, we present an efficient framework for editing the appearance of measured materials. First, we separate the material data into diffuse and specular parts, which is important for measured BRDFs since editing without distinguishing the two parts makes the results less predictive. Additionally, the albedo of each part is calculated for later color editing. Next, we use a novel method to cluster the materials by their gloss levels and select the representative material of every level. With this at hand, the roughness, i.e., shape of the specular highlights can be adjusted among different levels. Finally, we reconstruct the edited materials which can be directly used in both online and offline rendering. We test the framework on the MERL dataset to validate the effectiveness of our method.
Inductive Context-aware Process Discovery

Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A "control-flow first" approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control-flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We evaluate the approach using synthetic and real-world datasets and show that the resulting models are superior to state-of-the-art discovery methods in terms of measures that are based on multi perspective alignments.
Automatic Counting of Chili Ripeness on Computer Vision for Industri 4.0

This study aims to determine and counting the number of ripe chili on a tree. The proposed system uses images taken in gardens with a distance of 40 cm and an image resolution of $\mathbf{576x864}$. This work implements RGB2HSV color transformation, image segmentation, threshold, and image morphology, and then implements a Blob Analysis method for detecting and counting the ripe chilies on the trees. The trial results of this study provide a boundary for the blob area that will be used. Blob area less than 400 will not be labeled for the bounding box of the object detected. The accuracy of the proposed method to detect and calculate the ripe chilies on tress is 89.7%.
DMPR-PS: A Novel Approach for Parking-Slot Detection Using Directional Marking-Point Regression

The self-parking system plays an important role in autonomous driving, and one of its critical issues is parking-slot detection. Previous studies in this field are mostly based on off-the-shelf models designed for universal purposes, which have various limitations in solving specific problems. In this paper, we propose a parking-slot detection method using directional marking-point regression, namely DMPR-PS. Instead of utilizing multiple off-the-shelf models, DMPR-PS uses a novel CNN-based model specially designed for directional marking-point regression. Given a surround-view image I, the model predicts position, shape and orientation of each marking-point on I. From marking-points, parking-slots on I could be easily inferred using geometric rules. DMPR-PS outperforms state-of-the-art competitors on the benchmark dataset with a precision rate of 99.42% and a recall rate of 99.37%, while achieving a real-time detection speed of 12ms per frame on Nvidia Titan Xp. To make the results reproducible, the source code is available at https://github.com/Teoge/DMPR-PS.
Improving User Stories: A Case Study in the Chilean Banking Industry

Although a variety of user story refinement techniques have been proposed, there are still few empirical studies that assess their effectiveness in the industry. This paper reports the results of a mixed methods approach to evaluate the combined use of techniques for refining user stories in a Chilean banking organization. A combination of INVEST, 3 Cs and Specification by Example (SbE) techniques was proposed to improve the quality of user stories that were being generated in the organization during its transition from traditional development methods to agility. To validate the proposal, a comparison of case studies was carried out. A group of user stories that were created and refined with the method previously used in the organization was contrasted with a second and third group of stories for which the proposed technique was used. The results show that the combined use of INVEST, 3 Cs and SbE improved the user stories quality and was related to positive changes in the development team motivation.
Sizing, Design, and Modelling of Aerospace Electric Drive System with Long Feeder Cables

The aviation industry is tending towards more electric aircraft, replacing conventional pneumatic, hydraulic, and mechanical systems with electrical solutions. The electrification of actuators is motivated by increased efficiency, reduced maintenance and potential mass savings. Special considerations need to be made when designing an electric drive system with long feeder cables for aerospace. For a given actuation requirement, mass and volume need to be minimized while obeying thermal and reliability constraints. Fast switched high dV/dt voltages applied to a cable with poor machine-cable impedance matching can cause high-amplitude high-frequency voltage oscillations, potentially leading to corona discharge, insulation failure, high common-mode, differential-mode currents, bearing currents, and reduced system lifetime. Mitigation of these voltage oscillations can reduce and/or eliminate the problems associated with it. In this paper, the details of a high-level electric drive system design tool for component sizing and modelling is described. The designed system's mass, volume, reliability, and performance figures are produced. To demonstrate the sizing and design procedure, the results of a hypothetical 3kW fault-tolerant, multilane centralized electric drive system case study is presented. A LC inverter filter is used to mitigate transmission line effects due to long feeder cables.
Mental Arousal Level Recognition Competition on the Shared Database

This paper presents the results of the shared task with the aim of arousal level recognition for the competition held in conjunction with the 27th Iranian Conference on Electrical Engineering (ICEE 2019). A database annotated with arousal level labels released by Research Center for Development of Advanced Technologies. The contest was held on arousal database according to a defined protocol. Three teams were able to enter into the final stage of the competition according to compare their performance measure with the baseline method. The baseline method is proposed by the data owner. The aim of this paper is outlining the database, protocol design, and providing an overview of top-ranked algorithms proposed for the shared task.
Endoplasmic Reticulum Plays a Critical Role in Integrating Signals Generated by Both Biotic and Abiotic Stress in Plants

Most studies of environmental adaptations in plants have focused on either biotic or abiotic stress factors in an attempt to understand the defense mechanisms of plants against individual stresses. However, in the natural ecosystem, plants are simultaneously exposed to multiple stresses. Stress-tolerant crops developed in translational studies based on a single stress often fail to exhibit the expected traits in the field. To adapt to abiotic stress, recent studies have identified the need for interactions of plants with various microorganisms. These findings highlight the need to understand the multifaceted interactions of plants with biotic and abiotic stress factors. The endoplasmic reticulum (ER) is an organelle that links various stress responses. To gain insight into the molecular integration of biotic and abiotic stress responses in the ER, we focused on the interactions of plants with RNA viruses. This interaction points toward the relevance of ER in viral pathogenicity as well as plant responses. In this mini review, we explore the molecular crosstalk between biotic and abiotic stress signaling through the ER by elaborating ER-mediated signaling in response to RNA viruses and abiotic stresses. Additionally, we summarize the results of a recent study on phytohormones that induce ER-mediated stress response. These studies will facilitate the development of multi-stress-tolerant transgenic crops in the future.
An Overview of the CUREX Platform

Health sector is becoming more and more dependent on digital information every day. This fact can be exploited by cyber criminals who may obtain very lucrative benefits from stolen data. Moreover, a breach of integrity of health data can have terrible consequences for the patients. CUREX project aims to protect the confidentiality of health data and to maintain its integrity by producing a novel, flexible and scalable situational awareness-oriented platform. CUREX has been conceived as GDPR compliant by design. This design has been thought as a decentralised architecture enhanced with a private blockchain infrastructure. Thus, it ensures the integrity of the risk assessment process and of all data transactions.
Networked Distributed Fuzzy-Based Damping Controller for Wide-Area Power System Stabilization

In this work a novel networked distributed fuzzy control strategy is introduced to provide the stabilizing control commands to eliminate the low frequency electromechanical fluctuations occurred in geographically displaced networked electrical power grids featuring communication network channels limitations containing time delays and data packet loss. Firstly, with considering the common structure of networked systems, a distributed networked model is used for distributed networked power system (DNPS) in which the network delays and data packet dropouts are considered in Wide-Area Measurement System (WAMS). Then a wide area fuzzy-based stabilizer is proposed to deal with the influence of WAMS delays and damp the oscillatory dynamics. Hereof, a T-S fuzzy based controller is developed to describe the system dynamic and then an efficient distributed fuzzy controller with appropriate local feedbacks is used to compensate the nonideal manner of communication network channels. The developed method is exerted on a networked-based power system.
Rehabilitation Strategies for the Lower Limb Rehabilitation Robot with Magnetorheological Damper

Lower limb rehabilitation robot plays an important role in the rehabilitation of patients with lower limb disability, However, safety and controllability remain difficult to deal with. This article investigates a kind of exoskeleton robot with 4 Degrees of Freedom, which has adjustable length, magnetorheological dampers and wire-driven system for guarantee of safety and controllability. Then, a coupling strategy is proposed for interaction between the human and the robot utilizing AnyBody Modeling System. The driving function of the robot is designed based on the Fourier expansion. Kinematics analysis is carried out, and the results indicate that the correctness of the human-machine coupling and the lower limb rehabilitation robot meets the rehabilitation needs. Finally, the maximum muscle activity and the muscle contraction force, as the evaluation criteria for the effect of rehabilitation training, are measured under the combination of different limit angles and periods of lower limb motion. Simulation results show that 12°, 24° and 30° are more beneficial to the patients in the early, middle and late stages of rehabilitation during a 6-second training cycle.
Control and Operation of the Proposed Interlinking Converter in a DC-AC-DC Hybrid Microgrid Based on the Proposed Modes of Operation

Regarding improvement and development of new energy resources, hybrid distributed production resources are also being extended. Hybrid network includes AC microgrid and DC microgrid. These microgrids connect to each other via power electronic converter interface. DC microgrid having lower voltage level feeds loads that are more sensitive. To this aim, it connects to DC microgrid having higher voltage via an isolated DC/DC converter. Each microgrid's resources are controlled so it is possible to operate power sharing using droop method. The mentioned interlinking converter can transfer power in both directions and will operate either in voltage control or power control mode. In this paper we propose a method in which the interlinking converter will take part in microgrid power sharing alike to one the sources of each desired side. For the interlinking converter, two control loops are designed, one for current control and the other one for voltage control, and resonant controller is used in both loops. DC side resources are considered to be converter-based boost controlled resources. Double-lead integral controller will be designed for DC converter-based resources. AC microgrid resources are also considered to be converter-based resonant controlled resources. Simulation will be done in MATLAB, and sample network and defined scenarios will be tested considering operation mode on the network to investigate the performance of interlinking converter.
Electromagnetic Attenuation Factor Based NDE Approach for Depth Detection of Hidden Defects Using HTS rf-SQUID

We present a new approach for non-destructive evaluation (NDE) in homogeneous and isotropic metallic objects which contain defects at unknown depths based on single scan and multi frequency excitation. As known, there is an optimum frequency for each depth of defect. Finding the depth of an unknown defect requires us to find the optimal frequency. In conventional single frequency methods, the optimal frequency is obtained by applying a wide range of frequencies to the system separately and comparing the corresponding results in a time-consuming process. Conventional multi frequency inspections were introduced to obtain more information about test specimens. There are two ways to apply multiple excitation frequencies to the test sample. First way is to apply them sequentially to a single excitation coil which is still time consuming and the second method is to apply them simultaneously to multiple coils. In simultaneous excitation the testing time is shorter, but its processing of phase and amplitude signals is still challenging. We propose and examine the capability of the multi frequency eddy current (MFEC) method to detect the depth of hidden cracks in a shorter time period; using our HTS SQUID gradiometer-based system. The measurements are performed in a noisy environment, and a planar double-D shaped printed-circuit-board coil is used as the excitation coil. Comparing with the results of the consecutive single frequency excitation measurements, the obtained depths of the flaws using MFEC method was confirmed.
Persian Keyphrase Generation Using Sequence-to-Sequence Models

Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text. Keyphrase extraction is a useful upstream task and can be used in various natural language processing problems, for example, text summarization and information retrieval, to name a few. However, not all the keyphrases are explicitly mentioned in the body of the text. In real-world examples there are always some topics that are discussed implicitly. Extracting such keyphrases requires a generative approach, which is adopted here. In this paper, we try to tackle the problem of keyphrase generation and extraction from news articles using deep sequence-to-sequence models. These models significantly outperform the conventional methods such as Topic Rank, KPMiner, and KEA in the task of keyphrase extraction 1.
Perbandingan Keakuratan Hasil Peramalan Produksi Bawang Merah Metode Holt-Winters dengan Singular Spectrum Analysis (SSA)

Metode Holt-Winters digunakan untuk memodelkan data dengan pola musiman, baik mengandung trend maupun tidak . Terdapat dua metode peramalan dalam Singular Spectrum Analysis (SSA), yaitu metode rekuren ( R-forecasting ) dan metode vektor ( V-forecasting ). Metode rekuren melakukan kontinuasi secara langsung (dengan bantuan LRF), sedangkan metode vektor berhubungan dengan L-continuation . Perbedaan metode tentunya memberikan perbedaan dalam keakuratan hasil ramalan. Untuk melihat perbedaan antara ketiga metode tersebut dilakukan dengan melihat perbandingan keakuratan dan keandalan hasil ramalan. Untuk mengukur ketepatan peramalan digunakan Mean Absolute Percentage Error (MAPE) dan untuk mengukur keandalan hasil peramalan dilakukan dengan tracking signal . Aplikasi dilakukan pada produksi bawang merah Indonesia periode Januari 2006-Desember 2015. Peramalan kedua metode di SSA menggunakan window length L=39 dan grouping r=8. Dengan nilai α = 0.1, β= 0.001 dan γ=0.5, metode Holt- Winters additive memberikan hasil yang lebih baik dengan MAPE 13,469% dibanding metode SSA.
The Role of End-Winding in Building Up Parasitic Capacitances in Induction Motors

Modern variable-speed electrical drive systems have been widely used since the commercialization of power electronics-based frequency converters. Nevertheless, there are specific parasitic phenomena associated with inverter-induced bearing currents, which are always accompanying drive systems fed by pulse-width-modulated frequency converter outputs. In electrical machines, when considering capacitive bearing currents, the major role in forming the parasitic current path is played by the stray capacitance between the stator winding and the rotor surface. This capacitance can be divided in different proportions between the lamination stack zone and the end-winding part of the electrical machine depending on the machine power range. The outline of the current study is to investigate the share of the end-winding in building up the total winding-to-rotor capacitance as a function of the machine size.
Fast and Discriminative DC Grid Protection Using Bus-bar Capacitor Current

One of the main obstacles against DC grid implementation is DC grid protection against DC faults. A reliable DC circuit breaker as well as a fast and discriminative fault detection technique are required to f ulfill system safety and seamless operation. Additionally DC fault detection method should be capable to operate properly in the case of low or high impedance, transient or permanent fault events. This paper presents a new fault detection method based on using small DC bus capacitors located on the grid side of cable/line current limiting inductors. The benefits of proposed technique against rate of change of voltage (ROCOV) method are studied and presented. Smaller detection relay inductor, less sensitivity against noise, higher sensitivity in high impedance fault detection, and the capability of transient DC fault removal detection include major benefits. A 2 GW four terminal DC grid using symmetrical monopole modular multilevel converters (MMCs) is developed in PSCAD and simulation results are presented to demonstrate the technique properties.
Exploring Machine Learning Algorithms to Identify Heart Failure Patients: the Tuscany Region Case Study

Heart failure patients have become an important challenge for the healthcare system, since they represent a medical, social and economic problem. Early heart failure diagnoses can be very useful to improve patients' quality of life and to reduce the resources consumption, but they can be complex for the general practitioners. Data mining and machine learning techniques can really help in this field. The aim of this study is to validate some machine learning models to identify heart failure patients, starting from administrative data, and to make them transparent and interpretable. Despite the lack of clinical data, not available in Italy, but the most employed for the identification of heart failure patients, the results are comparable with the state-of-the-art ones and the models outperform the performances already obtained in Tuscany.
Real-Time Neural (RT-Neu) Spikes Imaging by a 9375 sample/(sec pixel) 32×32 pixels Electrolyte-Oxide-Semiconductor Biosensor

This paper presents a Real-Time Neural Spikes (RT-Neu) Imaging system on FPGA that processes and detects the electrical activity of a neurons population taken from rat hippocampi on an Electrolyte-Oxide-Semiconductor (EOS) Multi Electrode Array (MEA) local matrix of 32×32 pixels. RT-Neu has been implemented on Xilinx Zynq-7000 ARM/FPGA SoC. It receives the neural signals coming from a 9.375 kSample/(sec·pixel) 32×32 pixels EOS Biosensor, filters the single-pixel low-frequency offset/noise components and finally performs a multi-pixel signal processing (using a PCA-based correlation algorithm) to provide a final spatial map of the neural culture electrical activity. The correlation algorithm has been implemented to operate on multiplexed signals allowing to identify single neural Action Potentials (AP) with amplitudes as low as 215 μV0-PEAK. A dedicated GUI has been developed to monitor in real-time the neuron population electrical activity and whose demo video can be found at [1].