Miracle-dz/UniFAR-FTUs huggingface dataset:
FAQ Retrieval using Question-Aware Graph Convolutional Network and Contextualized Language Model

Frequently asked question (FAQ) retrieval, which seeks to provide the most relevant question, or question-answer (QA) pair, in response to a user's query, has found its applications in widespread use cases. More recently, methods based on bidirectional encoder representations from Transformers (BERT) and its variants, which typically take the word embeddings of a question in training time (or query in test time) as the input to predict relevant answers, have shown good promise for FAQ retrieval. However, these BERT-based methods do not pay enough attention to the global information specifically about an FAQ task. To cater for this, we in this paper put forward a question-aware graph convolutional network (QGCN) to induce vector embeddings of vocabulary words, thereby encapsulating the global question-question, question-word and word-word relations which can be used to augment the embeddings derived from BERT for better F AQ retrieval. Meanwhile, we also investigate leverage domain-specific knowledge graphs to enrich the question and query embeddings (denoted by K-BERT). Finally, we conduct extensive experiments to evaluate the utility of the proposed approaches on two publicly-available FAQ datasets (viz. TaipeiQA and StackF AQ), where the associated results confirm the promising efficacy of the proposed approach in comparison to some top-of-the-line methods.
Parametric Optimization of a Birdcage Resonator for Low-pressure Plasma Excitation

This paper deals with an analysis of the electric field inside birdcage resonators and discusses the geometric optimization of such resonators, emphasizing the electrical parameters. Firstly, we chose several different values of capacitors. For each capacitor value, parametric optimization was used to find the right geometry, for which the fundamental resonance mode lies at the frequency of interest (433 MHz). The numerical analysis results were employed as the basis for fabricating birdcage resonator prototypes. The prototypes’ parameters were measured using a vector network analyzer. Thus, we obtained a series of resonators working at similar frequencies (the deviation between the measured and the simulated values in dependence on the coil geometry is evaluated). The measured parameters and the simulated field distributions are compared and discussed with regard to the electrical properties of the resonators, electric field intensity, and homogeneity. The possible applications (such as an electric propulsion engine) are discussed.
Mandarin Singing Voice Synthesis with a Phonology-based Duration Model

Singing voice synthesis (SVS) systems are built to generate human-like voice signals from lyrics and the corresponding musical scores. In most SVS systems, a neural network-based auxiliary duration model is employed to control the duration of phonemes. In this paper, a rule-based algorithm inspired by Mandarin phonology is proposed for the duration modeling in Mandarin SVS. Specifically, the algorithm infers the duration of an “initial” consonant by looking up syllables in an existing training set that begin with the same consonant and have similar note lengths, and then computing the average consonant duration. Around this, we employ a combination of Tacotron2 and Parallel WaveGAN as the backbone of our SVS system for their favorable data efficiency on small datasets. Experimental results show that the singing voice synthesized by the proposed duration model is more expressive than that of a learning-based model. Moreover, since Mandarin is a tonal language, the inclusion of tonality consideration further enhances the naturalness of the generated voices.
Partial Fingerprint on Combined Evaluation using Deep Learning and Feature Descriptor

Partial fingerprint recognition has become crucial to identifying a user's authenticity in mobile device transactions. As a result, developments are increasing for more effective and accurate identification and authentication of a user using a scanner that captures a small fingerprint image. However, there is a reduction in the number of features from a full fingerprint to a partial fingerprint image during partial to partial fingerprint matching. Therefore, we propose a method combining deep learning and feature descriptors for partial fingerprint recognition. The matching score is obtained by the weighted combination of the scores from deep learning and feature descriptors. Experiments have been carried out with data variations such as the image size, epoch numbers and dataset types. The proposed method of combining deep learning and feature descriptors in the matching score evaluation process has obtained good results for the FVC2002 DB1, DB2 and DB3 datasets.
Parallel Implementation of CNN on Multi-FPGA Cluster

We developed a PYNQ cluster called M-KUBOS that consists of economical Zynq boards that are interconnected through low-cost high-performance GTH serial links. For the software environment, we employed the PYNQ open-source software platform. The PYNQ cluster is anticipated to be a multi-access edge computing (MEC) server for 5G mobile networks. We implemented the ResNet-50 inference accelerator on the PYNQ cluster for image recognition of MEC applications. By estimating the execution time of each ResNet-50 layer, layers of ResNet-50 were divided into four boards so that the execution time of each board would be as equal as possible for efficient pipeline processing. Owing to the PYNQ cluster in which FPGAs were directly connected by high-speed serial links, stream processing without network bottlenecks and pipeline processing between boards were readily realized. The implementation achieved 292 GOPS performance, 75.1 FPS throughput, and 5.15 GOPS/W power efficiency. It achieved 17 times faster speed and 86 times more power efficiency compared to the implementation on the CPU, and 3.8 times more power efficiency compared to the implementation on the GPU.
Advanced language model fusion method for encoder-decoder model in Japanese speech recognition

We apply an advanced language model fusion method to the encoder-decoder models of a Japanese automatic speech recognition (ASR) system to reduce character error rates (CERs) in cross-domain scenarios. Our method, which uses a Density Ratio approach based on Bayes' theorem, is an extension of the Shallow Fusion method widely used for integrating the scores of speech recognition and language models. Since Japanese has a much larger number of characters than alphabet languages, and since there can be multiple possible meanings and pronunciations of the same kanji character, it is unclear whether the linguistic information inside a character-based end-to-end speech recognition is approximated by the language model. We conducted experiments using two types of encoder-decoder models, an RNN model and a Transformer model, and evaluated our results by calculating the cross-domain CERs of the ASR ‘ s text output when using the Japanese Academic Presentation Speech (APS) corpus and the Japanese Simulated Presentation Speech (SPS) corpus. When using the RNN model, our proposed method achieved a 1.2 % lower CER in comparison to the Shallow Fusion method. When using the Transformer model, our method also outperformed the Shallow Fusion method, achieving a 0.7% lower CER.
Shallow Convolution Neural Network for Brain Tumor Classification using T1-weighted MRI scans

Recent advancements in deep learning has opened spaces for new research which is also well complimented with advancements in computational power. But it is still out of reach of many poor countries because of its cost. This work is in search of that solution. It is the view of the author that a lot of our problems can be solved through simple models. So in order to make it affordable shallow networks are designed. In this work, three shallow Convolutional Neural Network (CNN) i.e. Model 1, Model 2 and Model 3 and additionally three pre-trained model such as Resnet50, InceptionV3 and MobileNetV2 were used for classification of Brain tumor. Three classes of tumor are present namely Meningioma, Glioma and Pituitary. Model 1 showcased the best accuracy with 98.47%. Model 2 had an accuracy of 95.41%. Among the pre-trained model InceptionV3 had the highest accuracy of 92.36%.
A Study on Optimal Filter of Feedforward Active Noise Control System Based on Analysis of Frequency Response

In this paper we introduce an experimental study on an optimal filter of a feedforward active noise control (ANC) system based on the analysis of a frequency response. The feedforward ANC system reduces the unwanted noise by using a noise control filter based on an adaptive digital filter. The optimal noise control filter is determined by the transfer functions of each acoustic path including microphones and loudspeaker. However this optimal filter is a noncausal filter and cannot be used in the ANC system. In this paper the analysis of the optimal filter of the feedforward ANC system is introduced. Analytical results show that the optimal filter has a frequency characteristic similar to that of the adaptive noise control filter and the noncausal components mainly exist at low frequencies below the resonance of the secondary loudspeaker. Also two adjustments of the optimal filter are introduced to use the optimal filter in the ANC system. Simulation results show that the adjusted filter without the noncausal component has a frequency response similar to that of the original optimal filter. Also the simulation results show that the adjusted optimal filter can reduce the unwanted noise when using it as fixed noise control filter:
An A-$\Phi$ Formulation Solver in Electromagnetics Based on Discrete Exterior Calculus

An efficient numerical solver for the <bold>A</bold>-<inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> formulation in electromagnetics based on discrete exterior calculus (DEC) is proposed in this paper. The <bold>A</bold>-<inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> formulation is immune to low-frequency breakdown and ideal for broadband and multi-scale analysis. The generalized Lorenz gauge is used in this paper, which decouples the <bold>A</bold> equation and the <inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> equation. The <bold>A</bold>-<inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> formulation is discretized by using the DEC, which is the discretized version of exterior calculus in differential geometry. In general, DEC can be viewed as a generalized version of the finite difference method, where Stokes' theorem and Gauss's theorem are naturally preserved. Furthermore, compared with finite difference method, where rectangular grids are applied, DEC can be implemented with unstructured mesh schemes, such as tetrahedral meshes. Thus, the proposed DEC <bold>A</bold>-<inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> solver is inherently stable, free of spurious solutions and can capture highly complex structures efficiently. In this paper, the background knowledge about the <bold>A</bold>-<inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> formulation and DEC is introduced, as well as technical details in implementing the DEC <bold>A</bold>-<inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> solver with different boundary conditions. Numerical examples are provided for validation purposes as well.
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness

We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tasks in many science and engineering fields including signal processing, machine learning, and data mining. Although most existing studies focus on the clustering of nodes in static graphs, we often encounter time-varying graphs for time-series data, e.g., social networks, brain functional connectivity, and point clouds. In this paper, we formulate a node clustering of time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels. We solve the problem with a primal-dual splitting algorithm. Experiments on synthetic and real-world time-varying graphs are performed to validate the effectiveness of the proposed approach.
ADHD classification via auto-encoding network with non-imaging data fusion

With an existing binary hypothesis testing frame-work, we propose a modified auto-encoding (AE) network for ADHD classification using both Functional Connectivity (FC) and non-imaging data. For the presented AE network, it learns high-level features from the training FC data through a traditional auto-encoder. Here, a classification subnetwork is added to supervise the high-level features with their corresponding labels, thereby making these features more discriminative. The non-imaging data, including age, gender and Intelligence Quotient (IQ), are also used in this subnetwork. It further increases the subnetwork's robustness. We generate two sets of high-level features under the binary label hypotheses of test data, respectively. Then, a variability score comparison is provided on the high-level feature sets between two hypotheses. A label prediction of the test data is finally performed in terms of the comparison result. On the test of ADHD-200 datasets, our method achieves a high average accuracy of 99.7% and outperforms several state-of-the-art ADHD classification methods.
Reconfigurable metamaterial-based antenna using a novel class of miniaturized agile unit-cells.

Abstract In this thesis, a new class of reconfigurable and compact metamaterial unit-cells is introduced and used to present a novel technique for beam-switching antennas. Two metamaterial inspired antennas are designed and implemented using this technique at two different frequency bands i.e. S-band and millimeter-wave band. The prospective role of reconfigurable unit-cells in the future of metamaterial theory is investigated and a novel reconfigurable metamaterial unit-cell is designed that can provide a controllable refractive index. Moreover, a novel miniaturization technique is introduced that can be applied on any rectangular-shaped metamaterial unit-cell. This technique is analytically investigated and a step-by-step guideline is provided. A combination of these two concepts is exploited to design a controllable metamaterial medium that can be easily integrated in the structure of planar antennas. As the final objective of this work, the aforementioned artificial medium is embedded on the substrate of a planar dipole antenna and a radiation-pattern reconfigurable antenna with a switchable beam in the E-plane is designed and implemented in the S-band. The same technique is used at millimeter waves and an antenna with deflected beam in this band is presented. Both antenna structures are fabricated and measured to validate the simulation results. Also, the designed unit-cells in the S-band are fabricated and the performance of the unitcells is verified by a modified version of the well-known free-space measurement method. Two custom-made dielectric lenses are designed and fabricated for this method. Furthermore, an analytical model is derived to explain the beam-tilting effect produced by the metamaterial unit-cells. This model successfully describes the behavior of both antennas at the vicinity of the metamaterial unit-cells.
Research on Grid-based Abnormal Behavior Identification Method of Ships

To realize the identification of abnormal behaviors of ships, a grid-based method for identifying abnormal behaviors of ships based on the speed, course, and position data collected in a fixed time window is proposed. The density-based clustering algorithm is used to cluster the historical ship position data, realize the classification of the channel type and construct its grid parameter model. For the speed and course data after the mean value normalization and Box-Cox transformation, the parameter estimation method is used to identify the abnormal speed and heading. The ray method and density-based outlier detection algorithm are used to realize the identification of ships entering and exiting the detection waters and abnormal locations respectively. Taking the ship data downstream of Gezhouba as an example, the above method was tested and verified. The results show that the grid parameter model is suitable for the detection and identification of abnormal behavior of inland watercraft. Based on this model, the abnormal state of the ship's speed, course and position can be effectively identified, and the abnormal behavior of the ship can be accurately judged. The research results can provide technical support for inland watercraft navigation risk analysis, and forecasting and early warning.
Undetectable JPEG Image Batch Reversible Data Hiding with Content-adaptive Payload Allocation

As an efficient way to achieve covert storage in clouds, batch image data hiding attracts increasing attention in recent years. Reversible data hiding which can completely restore the original covers after data extraction brings about novel advantages for JPEG image-based covert storage. However, due to the specific manipulation on DCT coefficients, existing JPEG image reversible data hiding cannot meet the requirement of se-cure covert storage. Therefore, the undetectability of batch JPEG image reversible data hiding deserves investigation. In this paper, we propose an undetectable JPEG image batch reversible data hiding method, in which a content-adaptive payload allocation strategy is designed to enhance the undetectability and guarantee the reversibility. Experimental results show the superiority of this proposed method compared with the state-of-the-art related methods.
Comparative Study of Feature Extraction Method for Emotional Classification by Micro-expressions

Human facial expressions include a slight and instantaneous movement called micro-expressions. Unlike ordinary facial expressions, micro-expressions are impossible to control by oneself. Since micro-expression shows true emotions, micro-expression recognition is expected to play an active role in clinical diagnosis and business negotiations. However, it is difficult to recognize micro-expression because of their insensible and quick facial movements. In this study, we aimed to improve the accuracy of emotional estimation using micro-expressions. In previous researches on emotional estimation using micro-expression, LBP-TOP (Local Binary Pattern from Three Orthogonal Planes) and CBP-TOP (Centralized Binary Patterns from Three Orthogonal Planes) have been utilized. However, it is unclear if the feature selection and the combination of multiple features for emotional classification are effective. In this study, the emotional classification was performed using selected components of each individual feature. In addition, we investigated whether the fusion of scores obtained from each feature improved the accuracy of emotional estimation. The experimental results showed that the accuracy of emotional classification was increased by feature selection, whereas the score level fusion did not contribute to improve the performance of emotional estimation.
Data-Augumention Strategy For Small-Footprint Keyword Spotting

Due to the complex and variable environments of keyword spotting in real usage scenarios, there are multiple interference factors such as point-source noises, reverberation, background noises, dynamic changes in speaker's speed and volume. However, the current open source speech dataset lacks real data in such specific application scenarios. To solve this problem, we propose an efficient data modeling method in the real environment to process the data in the open source dataset to simulate the real use environment, so as to improve the robustness of the keyword spotting models. We evaluated our proposed data-augmentation strategy on the Mobvoihotwords dataset. Our results show that compared with the baseline system, the false rejection rate is reduced by 5.1% under the premise of a fixed false alarm per hour.
Multivariate Convolutional LSTMs for Relative Humidity Forecasting

The goal of predictive learning of temporal sequences is to forecast future values by learning from historical data. The accurate prediction of relative humidity using predictive models impacts the economic yield across various industries and sectors. Popular predictive models include recurrent neural networks. However, given the fact that relative humidity is not only dependant on its lag values, but rather, on other values such as dew point temperature, pressure and weather conditions in other areas, then such a predictive model can be boosted using a spatiotemporal grid with multiple variables in each cell. This paper presents a convolutional LSTM predictive model to learn complex multivariate and spatiotemporal correlation from a pseudo-image of meteorological variables taken from an area with multiple weather stations. Our model achieves superior performance compared to univariate and multivariate approaches. We show that using our method can predict far ahead into the future with competitive accuracy. Our method predicts relative humidity 10% better than univariate approaches, and outperforms current state-of-the-art methods by an average of 11% on test metrics.
Scheduling DAGs of Multi-Version Multi-Phase Tasks on Heterogeneous Real-Time Systems

Heterogeneous high performance embedded systems are increasingly used in industry. Nowadays, these platforms embed accelerator-style components, such as GPUs, alongside different CPU cores. We use multiple alternatives/versions/implementations of tasks to fully benefit from the heterogeneous capacities of such platforms and due to binary incompatibility. Implementations targeting accelerators not only require access to the accelerator but also to a CPU core for, e.g., pre-processing and branching the control flow. Hence, accelerator workloads can naturally be divided into multiple phases (e.g. CPU, GPU, CPU). We propose an asynchronous scheduling approach that utilises multiple phases and thereby enables a finegrained scheduling of tasks that require two types of hardware. We show that our approach can increase the schedulability rate by up 24% over two multi-version phase-unaware schedulers. Additionally, we demonstrate that the schedulability rate of our heuristic is close to the optimal schedulability rate.
Design of Linear Polarization Antenna with Wide Bandwidth and Low Mutual Coupling

In this paper, a linear polarization microstrip antenna (LPA) with wide bandwidth and low mutual coupling is proposed. Metal strip and metal baffle are introduced around the antenna fed by dual L-shaped probe, which increase the new resonance points and improve the isolation. The relative bandwidth of the designed LPA is about 50% (2.86 GHz–4.75 GHz) with the voltage standing-wave ratio (VSWR) <2.0. The isolation between adjacent elements can reach 25 dB in the whole working frequency band, and that near the center frequency point is 40 dB. The proposed LPA also has the characteristics of low profile and low cross polarization. Therefore, the proposed LPA with broadband and low mutual coupling characteristics has a very good application prospect in multi-input multi-output (MIMO) wireless communication system.