Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media

Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter’s sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.
Bag-of-Features Model for ASD fMRI Classification using SVM

Autism spectrum disorder (ASD) is an abstruse brain disorder in neuroscience research, which lead by challenges of social interactions, speech and nonverbal communication, and repetitive behaviors. Diagnosis of ASD is mostly based on behavioral analysis, which is time-consuming and depends on patient cooperation and examiner expertise. Recently, machine learning(ML) based algorithms are applied for ASD identification. However, there are some limitations using ML approaches such as investigation with functional magnetic resonance imaging (fMRI), depend on region-based analysis, and handling with the big dataset. In this work, we proposed a novel architecture based on the Bag-of-Features model for ASD classification to overcome these challenges. Firstly, we preprocess the images to extract the speeded-up robust features (SURF) from the selected feature point locations. The Bag-of-Feature extraction procedures include feature concatenation, select the most robust feature, and convert to feature vector. After that, we employ K-Means clustering to create a word visual vocabulary from the SURF vector. Then, we encode the Bag-of-Features by adopting coding and quantization techniques to get each class's indexed database. We prefer most tremendous machine learning methods SVM as ASD classifier. Finally, we independently evaluate our proposed architecture's performance using three different datasets from different fields, including ABIDE fMRI preprocessed images and subject's face images. In our experiments, weigh against other state-of-the-art methods that our ML classifiers with Bag-of-Feature extractors reinforce in medication and clinical purposes of ASD.
A Flower Classification Method Combining DenseNet Architecture with SVM

Identifying flowers becomes an urgent challenge due to the light intensity at different shooting angles, complex backgrounds and similarities of different flower species. A classification method DN-F-SVM based on combining DenseNet architecture with Support Vector Machine (SVM) is proposed for flower recognition. First a convolutional architecture DenseNet with the characteristics of dense connection mechanism and feature reuse is utilized to train data. Then, flower features obtained from the connection layer of DenseNet are extracted and efficient features are selected with feature selection method of Fast Correlation-Based Filter (FCBF), which combines with SVM to achieve flower classification. The proposed classification method DN-F-SVM has been trained on the Oxford-17 and Oxford-l02 flower data sets. It has delivered recognition rates of up to 99.12% and 98.90% which are higher than the existing methods, fully demonstrating its excellent recognition performance.
Contact Feature Recognition Based on MFCC of Force Signals

Contact-based tasks such as assembly and grinding often require the information on contact states. This letter therefore proposes a recognition method based on the Mel Frequency Cepstrum Coefficient (MFCC) of force signals. It demonstrates that the combination of MFCCs and time delayed neural networks is effective for learning features of contact events. This method is able to recognize instantaneous responses that do not generate repetitive waveforms. As a result, the recognition rate of the click response during a pen cap closing task increased from 75% to 96% following the proposed method. It is confirmed that this method is applicable not only to the data obtained by the robot's highly reproducible motions but also to the data whose parameters are scattered due to human interference.
Assessment of Combined SAR for Low Power Radio Base Stations with Multiple Antennas

In this paper, we investigate when the phase shift between the elements in an array of two dipole antennas can be disregarded for Specific Absorption Rate (SAR) assessments. It is found that at a short assessment distance (one tenth of wavelength from the phantom), the antennas behave like individual electromagnetic sources. Thus, the maximum of combined SAR when both antennas transmitting simultaneously is well-approximated by the maximum SAR of individual antennas. In addition, for antenna separation distances of half a wavelength or larger, the variation of SAR with the phase shift among the antennas is small. Therefore, measuring SAR with antennas operating simultaneously disregarding the phase shift provides an accurate estimate of the exposure leading to a significant reduction in the measurement time. The results obtained for the array of dipole antennas are verified by analyzing a low power Ericsson 5G radio base station product with four antenna branches.
Design Optimization of MV-NMOS to Improve Holding Voltage of a 28nm CMOS Technology ESD Power Clamp

An effective design for medium voltage ESD nMOS power clamp with layout modification of the source junction in 28nm high voltage CMOS technology is presented. Modification of N+/P+ source segmented design of ESD nMOS shows the most efficient ESD power clamp performance in terms of It2/area and holding voltage among other design structure experiments. With having similar It2 performance and area, the segmented GGNMOS has holding voltage of 1V higher than that of the base line GGNMOS for power pad protection. TLP, vf-TLP, HBM and DC-IV characterization techniques were used to characterize the structure.
Reducing BERT Computation by Padding Removal and Curriculum Learning

BERT [1] is very computationally expensive, which is a hurdle for its training and deployment. This work focuses on removing the unnecessary computation due to input padding in BERT. The input of BERT consists of two concatenated sentences. If the length of the two concatenated sentences is shorter than the maximum sequence length, padding must be added to the end of the sentences to fill the empty slots in the input. Because the lengths of sentences vary greatly, there can be a large amount of padding in input. For the English Wikipedia & BooksCorpus dataset, the percentage of padding among all the input tokens is 17% and 48%, respectively, when the max sequence length is set to 128 and 512. For the Chinese Wikipedia dataset, this percentage is 35% and 79%, respectively, when the max sequence length is 128 and 512. For SQuAD-v1.1 [2], padding accounts for 54% of the total input tokens when the max sequence length is 384. Thus, there is a lot of wasted computation on padding, which significantly increases the training and inference time.
Triple RSIW Fed Antipodal Vivaldi Antenna for Bandwidth Improvement

In this paper, it is presented a new modified structure of antipodal Vivaldi antenna (AVA). The structure is fed by a triple ridged substrate integrated waveguide (RSIW) on a single layer low-cost FR4 substrate. The ridges insertion allows a bandwidth enhancement due to the cut-off frequency decrease and consequently the miniaturization. The new structure is compared to an AVA fed by a classical SIW designed for a cut-off frequency around 4 GHz. Numerical simulations were performed and a bandwidth enhancement of 660 MHz was achieved with a gain of 2.06-7.59dBi within the frequency range.
Deep ConvLSTM-Inception Network for Traffic Prediction in Smart Cities

Accurate and real-time traffic prediction is essential for smart cities. However, due to the diversity of traffic point of interest in the city, the spatial correlation of urban traffic is extremely complicated. The use of ordinary spatial correlation feature extraction methods cannot cope with this complex spatial correlation. In addition, traffic flow changes also have temporal correlation. The traditional time series feature extraction method can’t extract the temporal correlation of traffic data well. Therefore, we propose a ConvLSTM-Inception Network(CL-IncNet) model for traffic prediction. The CL-IncNet uses the Inception module to extract the multi-granularity spatial correlation and uses the Inception module’s stack to extract the indirectly affected spatial correlation. In terms of extracting time-related features, we chose ConvLSTM structure. Compared with LSTM and GRU, this structure has fewer parameters and can guarantee the spatial relative relation of output information. The experimental results on the NYC-Taxi dataset show that our method outperforms four well-known baseline results.
Quality for Sustainability: SPC Analysis of Photovoltaic Modules Flash Test

This paper addresses the link between quality and sustainability in engineering systems and illustrates the role of quality tools in sustaining a high-level performance. It focuses on the application of Statistical Process Control (SPC) as a quality method at a PV (Photovoltaic) power plant that is set to generate the electricity from solar panels. In terms of performance, the PV plant has to meet certain contractual requirements including the level of delivered energy during the commissioning. Such level is highly impacted by the quality of the PV modules in terms of the solar energy generated from each module (based on the result of a flash test). The aim is to test the performance of two different PV technologies (i.e., thin film and crystalline silicon panels) that are supplied to the project from two different manufacturers during installation. To this end, SPC samples are drawn from the flash test results of the 39000 module supplied to the PV project. Results are analyzed and SPC control charts are developed to identify the special causes of process variability, explore variation patterns, and take remedial actions to ensure process stability. Results showed that even if the manufacturer supplied PV modules that are within the customer specification, it is not repress that the manufacturing process is statistically in control. SPC control charts have revealed multiple special causes in the process due to high variability in the power generated from the tested modules. These causes were carefully investigated and turned into opportunities for process improvement and stability. The study has highlighted the statistical challenges of the PV manufacturing process, as adopting SPC analyses will help manufacturer/suppliers to reduce the waste and improve the overall sustainability of the PV power plant.
Hybrid Model of Vehicle Recognition Based on Convolutional Neural Network

With the improvement of people’s living standard, the number of cars on the road has increased dramatically. Vehicle recognition is greatly significant for intelligent traffic management. In this paper, a hybrid model of vehicle recognition algorithm based on VGG16-softmax hybrid model is proposed. The convolutional neural network called VGG16 is used, Imagenet is used for pre-training, migration learning is used to migrate parameters to the new training model, variational auto-encoder is used for data reconstruction, and finally softmax multi-classifier is used for classification. Experiments show that this method can save time, get better vehicle feature of details and higher accuracy.
A 6-Bit 1.5-GS/s SAR ADC With Smart Speculative Two-Tap Embedded DFE in 130-nm CMOS for Wireline Receiver Applications

Implementing wireline receivers with a front-end analog-to-digital converter (ADC) allows for complex, flexible, and robust signal processing algorithms in the digital domain, as well as easy implementation of advanced modulation schemes beyond binary PAM2. However, the power consumption of the ADC and ensuing digital equalization is a key issue for such receivers in high-speed applications. Embedding analog equalization inside the ADC architecture allows for both a lower ADC resolution and a reduced-complexity digital equalizer, resulting in a more power-efficient receiver. This article presents a 6-bit 1.5-GS/s time-interleaved successive approximation register (SAR) ADC with low-overhead two-tap embedded decision-feedback equalizer (DFE). A smart speculative DFE is proposed to reduce additional conversion cycles required for the equalization realization in the ADC. Moreover, DFE functions are efficiently embedded in the capacitive digital-to-analog converter (DAC) references. The prototype ADC with two-tap DFE is implemented in a 130-nm CMOS process and achieves a 5.24-bit peak effective number of bits and 0.59-pJ/conversion-step figure-of-merit (FOM) at a 1.5-GS/s sampling rate while consuming 34.1 mW and occupying a core area of 0.32 mm2. The effectiveness of the embedded DFE in timing margin improvement is verified for 1.5-Gb/s operation over high-loss FR4 channels at a bit error rate (BER) of 10−9.
Cross-talk Mitigation Using Band-Stop Filters for Multiple-Frequency One-to-Multiple Inductive Power Transfer

Multiple-frequency inductive power transfer (IPT) system is able to independently charge multiple receivers with separated operating frequencies by using only one transmitting platform. Unfortunately, when the operating frequencies are close to each other, the system suffers from cross-talk among adjacent frequency signals. To mitigate the cross-talk, this work proposes using band-stop filters with isolation enhancement design in compensation circuit of the charging platform. Effectiveness of the proposed scheme is verified by circuit simulations and experiments with two-receiver IPT system operating at two adjacent frequencies of 170kHz and 200kHz.
A Review on Fuzzy Differential Equations

Since the term “Fuzzy differential equations” (FDEs) emerged in the literature in 1978, prevailing research effort has been dedicated not only to the development of the concepts concerning the topic, but also to its potential applications. This paper presents a chronological survey on fuzzy differential equations of integer and fractional orders. Attention is concentrated on the FDEs in which a definition of fuzzy derivative of a fuzzy number-valued function has been taken into account. The chronological rationale behind considering FDEs under each concept of fuzzy derivative is highlighted. The pros and cons of each approach dealing with FDEs are also discussed. Moreover, some of the proposed FDEs applications and methods for solving them are investigated. Finally, some of the future perspectives and challenges of fuzzy differential equations are discussed based on our personal view point.
Nanosecond Self-Q-Switching Nd:Luag Laser With High Repetition Rate

Numerical and experimental studies were investigated for the self-Q-switching Nd:LuAG laser which was achieved by the Kerr lens effect. By applying two output couplers of 6% and 1.4%, the shortest pulses of 193.4 ns and 5.1 ns were generated respectively while the corresponding repetition rates were 1.175 MHz and 96.15 MHz respectively. Related to the average powers of 3.34 W and 2.56 W, the system demonstrated the optical to optical efficiency of 21.87% and 21.1% for coupler of 6% and 1.4% respectively. The short pulses with the high repetition rates can be applied in many fields which demonstrate the high value of the research on this self-Q-switching laser.
Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening

Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. Methods: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. Results: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. Conclusions: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans.
Ultra-wideband, tightly-coupled magneto-electric phased array

Extensive researches in the past decade showed that in contrary to common intuition, a tightly coupled dipole array (TCDA) can offer extremely wide frequency bandwidth that is unattainable by a conventional resonant-type of phased arrays. This paper intends to show that TCDA is a form of CRLH structure and can be analyzed as such. Based on the CRLH theory, this paper presents a general form of closely-spaced phased array structure consisting of magneto (slots) and electric (planar monopoles) radiating sources. A general form of UWB active phased array structure based on such CRLH model is presented. Furthermore, it is shown that such arrays can be designed to suppress potential "surface waves" that exist in finite arrays. Advantage of this type of phased arrays include multi-octave broadband operation and does not require complex baluns or costly feed structure.
24-GHz Focused Beam 2 × 2 Patch Array with Coupling Strip for In-vehicle Surveillance

A focused beam 2×2 patch array is proposed and demonstrated for in-vehicle vital sign signal surveillance systems at 24 GHz. The design goal is to overcome the undesired defocusing effects caused by radome due to diffraction. To tackle the problem, a parasitic coupling strip is inserted between the pair of series-fed patch arrays. By properly controlling the length of conducting strip, the radiation from open ends of patches and coupling strips are added in phase to boost the peak gain at the zenithal direction. It thereby re-focuses the beam and alleviates EM wave leakage through windows of a vehicle. The design concept is illustrated in terms of parametric study and explained by simulated current distribution. In addition to S-parameters, radiation patterns are measured in terms of the normalized EIRP patterns taken from a fully-integrated active module for better approximating the real scenario. The experimental results clearly support the design concept.
An Online Reinforcement Learning Offloading Method for Delay-Sensitive Vehicular Service

Nowadays, various advanced vehicular applications are developed for road safety enhancement and traffic optimization, while the limited computation capability of vehicles can not always meet the delay requirements of these applications. Offloading the computation tasks to edge servers is regarded as a promising solution. However, how to select the optimal edge server to offload so as to support many delay-sensitive vehicular services is a challenging problem, especially when the server status changes. To overcome this challenge, this paper proposes an online reinforcement learning offloading method. The offloading decision process is divided into two steps to reduce the computation cost. Firstly, the edge server candidates are obtained through analysis on the historical server data, using the reinforcement learning method. Secondly, the optimal edge server is selected from the candidates online through observing current condition of the status of servers. Simulation results verify the superiority of the proposed method and it can effectively reduce running time.
CoCoPeLia: Communication-Computation Overlap Prediction for Efficient Linear Algebra on GPUs

Graphics Processing Units (GPUs) are well established in HPC systems and frequently used to accelerate linear algebra routines. Since data transfers pose a severe bottleneck for GPU offloading, modern GPUs provide the ability to overlap communication with computation by splitting the problem to fine-grained sub-kernels that are executed in a pipelined manner. This optimization is currently underutilized by GPU BLAS libraries, since it requires an approach to select an efficient tiling size, which in turn leads to a challenging problem that needs to consider routine, system, data, and problem-specific characteristics. In this work, we introduce an elaborate 3-way concurrency model for GPU BLAS offload time that considers previously neglected features regarding data access and machine behavior. We then incorporate our model in an automated, end-to-end framework (called CoCoPeLia) that supports overlap prediction, tile selection and effective tile scheduling. We validate our model's efficacy for dgemm, sgemm, and daxpy on two testbeds, with our experimental results showing that it achieves significantly lower prediction error than previous models and provides near-optimal tiling sizes for all problems. We also demonstrate that CoCoPeLia leads to considerable performance improvements compared to the state of the art BLAS routine implementations for GPUs.
Surface Defect Detection of Solar Cells Based on Multiscale Region Proposal Fusion Network

Manufacturing process and human operational errors may cause small-sized defects, such as cracks, over-welding, and black edges, on solar cell surfaces. These surface defects are subtle and, therefore, difficult to observe and detect. Accurate detection and replacement of defective battery modules is necessary to ensure the energy conversion efficiency of solar cells. To improve the adaptability to the scale changes of various types of surface defects of solar cells, this study proposed a multi-feature region proposal fusion network (MF-RPN) structure to detect surface defects. In such a network, region proposals are extracted from different feature layers of convolutional neural networks. Additionally, considering that multiple aspect ratios and scale settings and the use of multiple RPNs, result in an overlap of candidate regions and lead to information redundancy, we designed a multiscale region proposal selection strategy (MRPSS) to reduce the number of region proposals and improve network accuracy. Owing to the complete learning of shallow-detail texture information and deep semantic information, our multiscale RPN fusion structure can effectively improve an object’s multiscale feature extraction ability for various scales and types of surface defects of solar cells. Experimental results demonstrate that our method outperforms other methods by achieving a higher detection accuracy.