Miracle-dz/UniFAR-FTUs huggingface dataset:
A Co-Design Study Of Fusion Whole Device Modeling Using Code Coupling

Complex workflows consisting of multiple simulation and analysis codes running concurrently through in-memory coupling is becoming popular due to inherent advantages in online management of large-scale data, resilience, and the code development process. However, orchestrating such a multi-application workflow to efficiently utilize resources on a heterogeneous architecture is challenging. In this paper, we present our results with running the Fusion Whole Device Modeling benchmark workflow on Summit, a pre-exascale supercomputer at Oak Ridge National Laboratory. We explore various resource distribution and process placement mechanisms, including sharing compute nodes between processes from separate applications. We show that fine-grained process placement can have a significant impact towards efficient utilization of the compute power of a node on Summit, and conclude that sophisticated tools for performing co-design studies of multi-application workflows can play an important role towards efficient orchestration of such workflows.
Incorporation of Spiral Shaped Defected Ground Structure for Improving Radiation Characteristic of Planar Array-Antenna

This paper deals with the incorporation of spiral shaped defected ground structure (DGS) for improving radiation characteristic for planar array-antenna. The proposed planar array-antenna which is intended to operate at the frequency of 2.4GHz is designed on an FR4 epoxy dielectric substrate with the thickness of 1.6 mm and analyzed by use of a simulation software. It consists of 4× 4 elements where the microstrip line inset feeding method is employed to excite each antenna element. The characterization results show that the use of spiral shaped DGS yields the measured radiation characteristic to achieve the gain achievement of 10.47 dBi at the resonant frequency and the −10ddB bandwidth response of 140 MHz. These results are comparable to the simulated ones which are showing the potentiality of spiral shaped DGS incorporation for the radiation characteristics improvement.
Breathe to Dive: Exploring a Virtual Reality Game for Treatment of Cystic Fibrosis

Cystic fibrosis (CF) is a genetic disorder and chronic lung disease that affect between 70,000 and 100,000 people worldwide. Patients have to take medication and perform daily airway clearance exercises in order to mobilize sticky mucus from the lungs caused by CF. These exercises are rather repetitive and not very engaging causing patients to skip despite the health repercussions. In collaboration with a physiotherapist, we have developed a virtual reality diving game that diegetically incorporates the breathing exercise by translating it into movement through the underwater world. Initial user tests have shown that our combination of physiological input and virtual reality creates a compelling and immersive experience. However, it was also shown to be a task with widely differing perceived difficulty so that further steps need to be undertaken to adjust the game to various user groups.
Toward a Three-Way Image Classification Model: A Case Study on Corn Grain Images

Image processing techniques are essential in understanding and analyzing information based on pictorial data. The three-way decision is a natural extension of the widely accepted binary-decision model with an added third option. We propose a three-way image classification model in this article. The model integrates techniques of image processing and three-way decision for classification problems. The proposed model is performed in six steps, including HSV and extracted textual local binary pattern (LBP) information, which are: principal component analysis (PCA) feature extraction; decision table creation; classification space development; positive, boundary, and negative sets initialization and optimization; boundary reduction; and test verification. The proposed three-way decision model has been tested with a corn grain dataset under challenges such as (e.g. color component variation, surface pattern variation or so on), that achieved 99 percent performance accuracy.
Image Denoising Using Hybrid Singular Value Thresholding Operators

Truncated singular value decomposition (TSVD) is a simple and efficient technique for patch-based image denoising, in which a hard thresholding operator is utilized to set some small singular values to zero. Before performing the hard thresholding, the noise variance should be accurately estimated in order to determine the rank of the patch matrices. However, when the noise level is high, the denoisied results from the TSVD still contain some residual noise. To solve this problem, we present a hybrid thresheldoing strategy that combines a hard thresholding operator and a soft one. The former is directly reused the thresholding derived from TSVD, the latter is derived by minimum variance estimator. Simulation experiments are conducted to verify the effectiveness of the proposed method. Experimental results show that the method can effectively denoise the images with high level noise.
Seeing one thing and doing another: Contrast effects in automatic behavior.

Research on automatic behavior demonstrates the ability of stereotypes to elicit stereotype-consistent behavior. Social judgment research proposes that whereas traits and stereotypes elicit assimilation, priming of exemplars can elicit judgmental contrast by evoking social comparisons. This research extends these findings by showing that priming exemplars can elicit behavioral contrast by evoking a social comparison. In Study 1, priming professor or supermodel stereotypes led, respectively, to more and fewer correct answers on a knowledge test (behavioral assimilation), but priming exemplars of these categories led to the reverse pattern (behavioral contrast). In Study 2, participants walked away faster after being primed with an elderly exemplar. In Study 3, the proposition that contrast effects reflect comparisons of the self with the exemplar was supported.
Nonlinear Error Compensation of Capacitive Angular Encoder

Angular encoder are widely used in industrial fields. In addition to the true angle, the output of the encoder is superimposed with nonlinear errors caused by installation, processing, demodulation circuits, etc. The methods such as least square method (LSM) and back propagation neural network (BP-ANN) cannot effectively identify and compensate the nonlinear errors. Aiming to solve the problem, the method of support vector machine (SVM) is proposed to achieve the nonlinear error compensation. Furthermore, the adopt particle swarm optimization (IPSO) is applied to determine the parameters of SVM for further improve the precision of compensation model. The encoder is applied to verify the validity of the algorithm and experimental results show that the IPSO-SVM method can compensate the nonlinear and reduce form 0.08° to 0.0004°, and it is an effective method for compensation the nonlinear error of capacitive angular encoder.
A Review Semantics Based Model for Rating Prediction

A review expresses the concerned aspects and corresponding assessments a customer has towards a particular item. Extracting the user’s interests and product’s features from their aggregated reviews and matching them together to predict the overall rating is a common paradigm in a review-based recommender. However, such a paradigm trains model on the aggregated historical review which grows with time and may have much conflicting semantics, thus the scalability and accuracy may be compromised. In this paper, a novel review semantics based model(RSBM) is proposed to enhance the performance of the review-based recommender. It consists of three parts: the review semantics extractor, the review semantics generator and the rating regressor. Firstly, the review semantics extractor uses a convolutional neural network(CNN) to extract the semantic features of a particular review text. Secondly, the semantics generator uses a memory-network liked structure and attention mechanism to simulate the decision-making process which assesses each concerned aspect of an item to generate the review semantics. In the training phase, the generated semantics is compared with the semantics extracted by the review semantics extractor. In the last, the generated semantic features are fed into the rating regressor to predict the overall rating. Experiments on a series of reality datasets show that the proposed model gains better performances than several state-of-the-art recommendation approaches in terms of accuracy and scalability.
Evaluation of Oil Workers' Performance Based on Surveillance Video

We present our research on the applicability of computer vision techniques for extracting various oil workers' performance metrics. This paper focuses on learning two metrics associated with the workers' location. The first metric $\boldsymbol{e}_{1}$ is the percent of frames in which only some part of the crew is present. If its value is bigger than some threshold value, the crew's performance is declared inefficient. We propose to perform human detection in each video frame and count people present in order to calculate $\boldsymbol{e}_{1}$. The Faster R-CNN and single-shot detectors with several types of feature extractors were tested on a specially collected dataset. By finetuning the most accurate of them we've achieved 0.99 precision and 0.91 recall. The second metric $\boldsymbol{e}_{2}$ considers workers' distance from an automated gas control system, which is the main subject of maintenance. We propose using some markers on the uniform for worker recognition and estimation of his/her position relative to an automated gas control system. We've tested the ArUco and the RUNETag markers on synthetic data and proved that they cannot be applied to our problem. We've also carried out some preliminary research on uniform numbers detection, as they can be also considered as markers. The Connectionist Text Proposal Network (CTPN) used for text detection achieved an accuracy of 0.76. Text recognition performed by Tesseract OCR failed with 0.05 recall. However, we plan to collect a dataset for number detection and recognition in the future and test more approaches.
Flexible and Easy-to-fabricate Electrochemical Sensors Integrating an Absorption Layer with Paper Microfluidic

Electrochemical sensors can provide numerous health information while analyzing ions in bio-fluids. Ion selective electrodes (ISEs) have become ideal sensing devices for measuring cation and anion concentrations in aqueous samples. In the scenario for human bio-fluidics detection, substrate should be flexible in comply with body and hydrophilic to enrich fluidics in natural perspiration analysis in a possible way. Here graphite paper ISE combined with paper microfluidic is presented to meet both requirements. The ion-to-electron transducer of the ISE electrodes was achieved by subjecting PEDOT: PSS and carbon nanotubes solutions on the top of conducting layer (Ag, Graphite paper) accordingly, through which potential drift can be minimized. A no-mask process including paper-cutting, wax-printing and cast-dropping, has also been introduced to realize the easy fabrication for lab experiments and future development.
Oasis: Online Analytic System for Incivility Detection and Sentiment Classification

Incivility detection is an important task to identify offensive language on online social media platforms. Sentiment analysis is an essential task of natural language processing to identify the emotions of given sentences. In this demonstration, we propose an online processing system, called Oasis, to perform incivility detection and sentiment prediction for short texts (e.g., tweets). Oasis offers several useful features including the CNN-LSTM classification models, incivility detection and sentiment analysis for real-time tweets and user-interested queries, and personalized search of trending topics in Twitter.
Experimental Verification of Efficiency of Some Composite Algorithms

The paper contains experimental analysis of efficiency of a new class of algorithms i.e. composite algorithms aimed at solving of knapsack problem providing global optimal solution. These algorithms combine features of branch and bound algorithms together with branch and cut procedure – as latter is used dynamic programming: verified are modifications of branch and bound procedures, which cut off "bad" vectors of variables technique includes technologies used in dynamic programming, and vice versa - modifications of the algorithm, which implements dynamic programming, are involving bounds determination procedures used by branch and bound methods. It is shown experimentally that, in the case of the knapsack problem, the efficiency of these algorithms exceeds the effectiveness of the parent procedures, and this difference increases with the number of variables
Heterogenous Information Network Embedding Based Cross-Domain Recommendation System

The data sparsity or the cold start is a general bottleneck of recommendation service systems. Two types of state-of-art schemes address this issue via enriching knowledge. Cross-domain recommendation schemes transfer knowledge from another dense domain, but they only explore a single kind of shared knowledge. Recommendation schemes built on heterogeneous information networks (HIN) utilize knowledge implied in meta-paths connecting various objects, these methods, however, investigate meta-paths in a single domain. To improve recom-mendation performance in a highly sparse scenario, we propose a HIN Embedding based Cross-domain Recommendation (HecRec) framework, which exploits cross-domain information by establishing meta-path based HIN embeddings in both the source and the target domain and conducts personalized recommendation by integrating the obtained HIN embeddings with a rating predictor. To make the best use of cross-domain information and avoid the knowledge confliction between knowledge from different metapaths observed in real-system datasets, we adopt a concept of "overpass bridge" to integrate the HIN embeddings drawn via different meta-paths. Experiments on two public datasets, i.e., MovieLens and LibraryThing, demonstrate the capacity of HecRec on addressing data sparseness. The recommendation performance of HecRec is improved up to 6.9% in MAE and 5.5% in RMSE compared with the state-of-art schemes in the coldest cases.
Numerical Braille Module for Learning Simple Mathematical Operations

Individuals with a good understanding of mathematics have better chances to do well in their life. However, it is an extremely challenging task for people with visual disabilities to learn and execute simple mathematical operations. Although there are many mathematical learning products in the current market, most of them are expensive, with smooth input keys, liquid crystal display and audio outputs which limit the learning process of numerical braille code. In this paper, we present a numerical braille module which aimed to help people with visual challenges to learn simple mathematical operations. This module comprises 12 miniature solenoids that were controlled by an Arduino Uno microcontroller with a numeric keypad that serves as an input unit. The verification of input and output signals was conducted to ensure that all the inputs are correctly stored in Arduino board memory to perform a correct operation and raised the correct solenoids to produce a braille code pattern. Three operations were tested, which are addition, subtraction and multiplication. The developed numerical braille module was capable of performing simple calculations tasks and raised correct solenoid output accordingly. The outcome of this work provides an optimistic prospect of attracting the visually impaired to learn and perform mathematical operations.
Excitation Method of Cavity Using Characteristic Modes

This paper presents characteristics of cavity eigenmode, and a selective excitation method of an internal mode, using TCM. The results of CM analysis of the cavity, with an aperture show that cavity modes are separated into the internal and external modes. Two-inverted-F antennas are placed at the diagonal line on cavity bottom surface. This feeding probe is an inductive coupling element (ICE), not to use additional matching circuits.
Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack detection, being able to present better results in some scenarios than classic intrusion detection systems such as Snort or Suricata. In this sense, this research focuses on the evaluation of characteristics for different well-established Machine Leaning algorithms commonly applied to IDS scenarios. To do this, a categorization for cybersecurity data sets that groups its records into several groups is first considered. Making use of this division, this work seeks to determine which neural network model (multilayer or recurrent), activation function, and learning algorithm yield higher accuracy values, depending on the group of data. Finally, the results are used to determine which group of data from a cybersecurity data set are more relevant and representative for the intrusion detection, and the most suitable configuration of Machine Learning algorithm to decrease the computational load of the system.
Error Bound Analysis of Chip-to-Chip Communication Based on Spectrum Shaping

Spectrum shaping is a good candidate for improving the efficiency of chip-to-chip communication systems. In recent years, distinct spectrum shaping schemes have been proposed for different chip-to-chip communication channels. However, a general metric on the suitability of spectrum shaping schemes is still lacking. This work addresses the problem through theoretical analysis. Two methods are utilized in the investigation, capacity achievement and error probability minimization. For the first time to our knowledge, the relation between bit error probability (BEP) and spectrum shaping is analyzed for chip-to-chip communication systems. The results facilitate the error bounds calculation of different spectrum shaping schemes, which provides a good reference for the design on the strategy of spectrum shaping.
Location Detection Method for Contactless Power Supply Vehicles Based on Equivalent Impedance Transform

Contactless power supply for rail transit vehicles has good prospects for development. Accurate detection of vehicle position to guide power supply is a key techinique for contactless power supply. Traditional photoelectric induction and electromagnetic position detection methods have some defects, such as low detection accuracy and large external interference. Since the equivalent impedance of coil will change before and after the coil enters the ground coil . A method of transmitting narrow pulse to the front-end inverter of transmitting coil for contactless power supply vehicle is proposed, In this way, the current of the primary coil can be detected to determine whether the vehicle enters the ground coil or not. The measured current changes are used to judge whether the ground coil is fully put into operation or not. Finally, the feasibility and validity of this method are verified by experiments.
Channel Estimation and Bit Loading for WPM with Time-Domain MMSE Equalization

Wavelet Packet Modulation (WPM) is a Multi-Carrier Modulation (MCM) technique considered as a possible substitute for Orthogonal Frequency Division Multiplexing (OFDM). Unlike OFDM, symbols of WPM overlap in the time domain, preventing the use Cyclic Prefix (CP) for equalization. This paper uses Minimum-Mean Square Error (MMSE) equalization for WPM considering unknown Channel State Information (CSI). A channel estimation method is proposed based on solving an overdetermined system of linear equations. The Channel Impulse Response (CIR) is estimated using a training sequence. The estimated CIR is used for bit loading that considers limitations of MMSE equalization such as noise amplification and signal attenuation. Numerical results based on quasistatic channels with Rayleigh fading demonstrate that the Bit Error Rate (BER) for WPM is lowered with the proposed bit loading. In addition, with unknown CSI, WPM with bit loading outperforms OFDM with bit loading.
A Compact 10-bit Nonbinary Weighted Switched Capacitor Integrator Based SAR ADC Architecture

A compact switched capacitor integrator (SCI) based successive approximation register (SAR) analog to digital converter (ADC) for data acquisition system is presented. This technique requires an operational transconductor amplifier (OTA), a comparator and four equal sized capacitors of moderate value for fully differential approach and the architecture is resolution independent. The reference voltage is generated by charge sharing between a reference capacitor and the input capacitor of a switched capacitor (SC) integrator. The DAC voltage for comparison is generated by accumulating the charges on the SC integrating capacitor. ADC being fully differential nature has wide input range and it is parasitic insensitive to a large extent. As a stand alone data converter it has small capacitance spread and hence its input capacitance is easy to drive. A 10 bit 0.9MHz sampling rate SAR ADC is designed using 180 nm CMOS technology, operating at 1.8 V supply, has effective number of bits (ENOB) of 9.5 at Nyquist frequency. The ADC occupies small die area compared to SAR with a binary weighted capacitor array.
Linear Time S-Component Extraction for General Petri Nets

In this work, we present a linear time, S-component extraction algorithm capable of handling strongly connected, General Petri Nets. Most prior works have focused exclusively on Free-Choice or Extended Free-Choice Nets. We generalise S-component extraction to General Petri Nets, providing a fast transformation between event and state-based spaces. This work is directly applicable to Petri Net based logic synthesis, as a Petri Net event model describing a system's behaviour, may be mapped to a set of S-components, and eventually interacting on Multiple Synchronised FSMs (MSFMSs) [1], implemented in synchronous or asynchronous Boolean Logic. We have tested a set of 25 Petri Net benchmarks, including FSMs, Marked Graphs, Free-Choice, Extended Free-Choice, Asymmetric Choice and General Petri Nets. Our algorithm managed to successfully generate correct S-covering results for all of them. We present results against the original algorithm of [2]. The S-component extraction algorithm complexity is $O(\vert P\vert +\vert T\vert +\vert F\vert)$.
Derived Tree Block Partition for AVS3 Intra Coding

AVS3 is the next generation video coding standard and is being developed by the audio and video standard (AVS) working group of China. As the successor of AVS2, AVS3 is designed to achieve a great coding efficiency beyond AVS2 as well as H.265/HEVC. In this paper, we propose a novel block partition method for intra coding, named derived tree block partition (DTBP), based on the block partition structure in AVS3. By further splitting a leaf node of the partition tree to quad split types or asymmetric types, the signaling efficiency and the flexibility of partitioning asymmetric texture can be improved. When implemented on HPM3.1, up to 1.14% and an average 0.61% BD-rates are achieved under All-Intra configuration.