Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Study of High-Power Half-Bridge Interleaved LLC Converter

Equalizing and minimizing output current ripple in an interleaved half-bridge LLC converter is explored to raise output power with high efficiency. A strategy to let the half-bridge LLC work in regions 1, ω > ωr1, is implemented to ensure the two interleaved duty cycle close to 50%, which makes the output current width continuous link without lag and also reduce the component size. In addition, the magnetizing currents can also be equal to mitigate the circulation current in the MOSFETs. In transformer design, the secondary winding is wound with equal-length wire to lead the amplitudes of the full-wave output current ripple almost the same, which equalizes the wire thermal to improve the power efficiency. Further, with sharing the output current, an interleaved half-bridge LLC converter can perform a power conversion up to 2.7 kW with the highest efficiency close to 98%. Besides, in-phase and 90°-phase-shift output currents merging to reduce the output ripple currents are respectively explored in experiment. In this paper, a simple modeling of the interleaved LLC is also explored and a design consideration for implementing a 2.7 kW high-power interleaved half-bridge LLC converter is described. All experimentations including the switching statuses and temperature distribution are displayed and discussed in details.
Exploiting Deep Representations for Neural Machine Translation

Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures. However, only the top layers of encoder and decoder are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to simultaneously expose all of these signals with layer aggregation and multi-layer attention mechanisms. In addition, we introduce an auxiliary regularization term to encourage different layers to capture diverse information. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation data demonstrate the effectiveness and universality of the proposed approach.
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning

Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training

It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording. Although much attention has been paid to this task and promising progress is made, there exist notable gaps between automatically generated ones with those created by humans, especially on the aspects of term novelty and thematic consistency. Towards filling the gap, in this paper, we propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation, where the autoencoder part generates poems with novel terms and a discriminator is applied to adversarially learn their thematic consistency with their titles. Experimental results on a large poetry corpus confirm the validity and effectiveness of our model, where its automatic and human evaluation scores outperform existing models.
Automating Generation of Low Precision Deep Learning Operators

State of the art deep learning models have made steady progress in the fields of computer vision and natural language processing, at the expense of growing model sizes and computational complexity. Deploying these models on low power and mobile devices poses a challenge due to their limited compute capabilities and strict energy budgets. One solution that has generated significant research interest is deploying highly quantized models that operate on low precision inputs and weights less than eight bits, trading off accuracy for performance. These models have a significantly reduced memory footprint (up to 32x reduction) and can replace multiply-accumulates with bitwise operations during compute intensive convolution and fully connected layers. Most deep learning frameworks rely on highly engineered linear algebra libraries such as ATLAS or Intel's MKL to implement efficient deep learning operators. To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries. In this paper we introduce a work flow to quickly generate high performance low precision deep learning operators for arbitrary precision that target multiple CPU architectures and include optimizations such as memory tiling and vectorization. We present an extensive case study on low power ARM Cortex-A53 CPU, and show how we can generate 1-bit, 2-bit convolutions with speedups up to 16x over an optimized 16-bit integer baseline and 2.3x better than handwritten implementations.
Cross-topic Argument Mining from Heterogeneous Sources

Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics. We show that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs by more than 3 percentage points in F1 in two- and three-label cross-topic settings. We also show that these results can be further improved by leveraging additional data for topic relevance using multi-task learning.
Neural Related Work Summarization with a Joint Context-driven Attention Mechanism

Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data

Natural language inference has been shown to be an effective supervised task for learning generic sentence embeddings. In order to better understand the components that lead to effective representations, we propose a lightweight version of InferSent, called InferLite, that does not use any recurrent layers and operates on a collection of pre-trained word embeddings. We show that a simple instance of our model that makes no use of context, word ordering or position can still obtain competitive performance on the majority of downstream prediction tasks, with most performance gaps being filled by adding local contextual information through temporal convolutions. Our models can be trained in under 1 hour on a single GPU and allows for fast inference of new representations. Finally we describe a semantic hashing layer that allows our model to learn generic binary codes for sentences.
Temporally Grounding Natural Sentence in Video

We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification

Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time. Non-autoregressive models enable the decoder to generate all output symbols independently in parallel. We present a novel non-autoregressive architecture based on connectionist temporal classification and evaluate it on the task of neural machine translation. Unlike other non-autoregressive methods which operate in several steps, our model can be trained end-to-end. We conduct experiments on the WMT English-Romanian and English-German datasets. Our models achieve a significant speedup over the autoregressive models, keeping the translation quality comparable to other non-autoregressive models.
Automated-Sampling-Based Stability Verification and DOA Estimation for Nonlinear Systems

This paper develops a new sampling-based method for stability verification of piecewise continuous nonlinear systems via Lyapunov functions. Depending on the nonlinear system dynamics, the candidate Lyapunov function and the set of states of interest, verifying stability requires solving complex, possibly non-convex or infeasible optimization problems. To avoid such problems, the proposed approach firstly distributes the verification of Lyapunov's inequality on a finite sampling of a bounded set of states of interest. Secondly, it extends the validity of Lyapunov's inequality to an infinite, bounded set of states by automatically exploiting local continuity properties. A sampling-based method for estimating the domain of attraction (DOA) via level sets of a validated Lyapunov function is also presented. Efficient state-space exploration is achieved using multi-resolution sampling and hyper-rectangles as basic sampling blocks. The operations that need to be performed for each sampling point in the state-space can be carried out in parallel, which improves scalability. The proposed methodology is illustrated for various benchmark examples from the literature.
A Paper Recommendation System Based on User's Research Interests

Researchers and scientists read articles to improve their studies. Researchers spend too much time and struggle to find the suitable article they are looking for. The purpose of article recommendation system is to reduce the time they spend and present to them the related articles they are not aware of. Classic article recommendation systems do not consider the user's information, they show the same results in the same sort for each researcher. In this study, an article recommendation system that takes into consideration the researcher's work field and the publisher's previous articles is presented. One of the most important innovations of this work is the use of TF-IDF and Cosinus similarity to make article recommendations taking user's past articles into consideration. As a result of the work, the users have been recommended articles, and the method we present has proved more successful results compared to equivalent methods according to f-Measure criterion.
Study of the Demagnetization Fault in an AFPM Machine in Relation with the Magnet Location

This paper studies the totally demagnetization fault in an Axial Flux Permanent Magnet Synchronous Generator using 3D FEM. In the examined cases two magnets are demagnetized which are located in different locations on the machine. The EMF and stator current waveforms are compared for each case, while the corresponding harmonic spectrums are investigated in order to export the characteristic fault harmonics.
How Well Did You Locate Me? Effective Evaluation of Twitter User Geolocation

We analyze fifteen Twitter user geolocation models and two baselines comparing how they are evaluated. Our results demonstrate that the choice of effectiveness metric can have a substantial impact on the conclusions drawn from an experiment. We show that for general evaluations, a range of metrics should be reported to ensure that a complete picture of system effectiveness is conveyed.
Using the Goertzel Algorithm Over Disjoint Narrow Frequency Bands for Fault Diagnosis of Induction Motors

Induction motors (IMs) are the driving force of modern industries. On-line condition monitoring of IMs tries to detect faults at early stage helping to adjust the maintenance schedule and thus, avoiding unexpected and costly breakdowns. Among the methods for condition monitoring, on-line motor current signature analysis is attracting a rising interest, because it is non-invasive and it can identify a wide variety of faults. Usually, motor current signature analysis (MCSA) approaches are Fourier based methods, which require long acquisition times (to achieve high spectral resolution) and high sampling frequencies (to reduce aliasing effects). However, these requirements generate a huge amount of data which can cause an overwhelming burden on embedded devices with very limited internal memory such as digital signal processors (DSPs) or field programmable gate arrays (FPGAs) among others. To address these issues, this paper introduces an approach which enables to carry-out fault diagnosis of IMs based on MCSA but using low cost computing devices, with limited memory and computing power. This approach proposes the use of disjoint narrow bandwidths and a modified sliding window fast Fourier transform (SFFT) based on the Goertzel algorithm. The modified SFFT proposed uses small data buffers to calculate the signal's spectrum. Besides, the use of disjoint narrow frequency bands highly reduces the number of bins being updated as each new data is sampled for fault diagnosis purposes. Based on this approach, an embedded device only needs to store and process a minimal set of bins, compared with other methods, for a desired resolution.
Automatic Identification of S1 and S2 Heart Sounds Using Simultaneous PCG and PPG Recordings

Accurate and reliable identification of the first (S1) and second (S2) sounds of the phonocardiogram (PCG) is still a challenging task due to the presence of the S3 and S4, murmurs, high-pitched sounds, physiological interference, and other environmental noises. This paper presents an automated method for identification of the S1 and S2 sounds by simultaneously recording, processing, and fusing the extracted fiducial points of the PCG and photoplethysmogram (PPG) signals. The method consists of four main stages: the PCG and PPG signal sensing with Arduino interfacing, the heart sound delineation (HSD) and pulse waveform delineation (PWD) using variational mode decomposition (VMD), and the S1/S2 identification based on the estimated timings of the pulse onset, peak, upward slope zerocrossing, and downward slope zerocrossing of the pulsatile waveform. The accuracy and robustness of the HSD and PWD algorithms are evaluated using both the benchmark databases and simultaneous PCG and PPG recordings. Then, the proposed S1/S2 identification method is evaluated using the simultaneous PCG and PPG recordings. The VMD-based HSD algorithm achieves an average sensitivity (SE) = 99.80%, positive predictivity (PP) = 99.0%, and accuracy (ACC) = 98.81% whereas the Gaussian derivative filter (GDF)-based HSD and bandpass filter (BPF)-based HSD algorithms achieve the average SE = 99.75%, PP = 93.59%, and ACC = 93.37% and the average SE = 99.67%, PP = 84.97%, and ACC = 84.73%, respectively, for the recorded PCG signals with SNR value of 5 dB. The proposed S1/S2 identification method achieves a correct identification rate of 99.75% which outperforms the GDF+systole interval(SI)/diastole interval(DI) and the BPF+SI/DI feature-based methods having 66% and 59.25%, respectively, in the case of noisy recordings with a SNR value of 5 dB. The proposed method has great potential in improving the identification accuracy and robustness in the presence of other sounds and murmurs, and different kinds of environmental noises.
Emotion-based Dynamic Difficulty Adjustment Using Parameterized Difficulty and Self-Reports of Emotion

Research has shown that dynamic difficulty adjustment (DDA) can benefit player experience in digital games. However, in some cases it can be difficult to assess when adjustments are necessary. In this paper, we propose an approach of emotion-based DDA that uses self-reported emotions to inform when an adaptation is necessary. In comparison to earlier DDA techniques based on affect, we use parameterized difficulty to define difficulty levels and select the suitable level based on players' frustration and boredom. We conducted a user study with 66 participants investigating performance and effects on player experience and perceived competence of this approach. The study further explored how self-reports of emotional state can be integrated in dialogs with non-player characters to provide less interruption. The results show that our emotion-based DDA approach works as intended and yields better player experience than constant or increasing difficulty approaches. While the dialog-based self-reports did not positively affect player experience, they yielded high accuracy. Together, these findings indicate our emotion-based approach works as intended and provides good player experience, thus representing a useful tool for game developers to easily implement reliable DDA.
Using Applied Games to Engage mHealth Users: A Case Study of MindMax

The current trend to use applied games to engage users with mobile health (mHealth) delivery systems continues to build, yet research as to its effectiveness is still sparse. This study evaluates the effectiveness of using two different casual games to drive meaningful engagement with an mHealth app. MindMax was produced by the Australian Football League Players Association to improve the mental health and wellbeing of young Australians. Interviews (N = 42) and app usage data of organic users (N = 2679) suggest that MindMax has sustained users' interest with the app by using the casual games as rewards for engagement. In addition, there is evidence that the gamification strategy was successful in drawing users back to the wellbeing modules. Mixed experiences with the more difficult game suggest the potential usefulness of game play data to inform more personalized mHealth messaging. Further uses for applied games in mHealth applications are discussed.
Automatic Resonance Frequency Tuning Method for Repeater in Resonant Inductive Coupling Wireless Power Transfer Systems

Intermediate resonator (repeater) in resonant inductive coupling wireless power transfer (RIC-WPT) systems can increase the transmission distance between the transmitting and receiving coils. The induced current in the repeater is easily affected by the frequency splitting phenomenon because the quality factor (Q-factor) of the repeater is generally as high as several hundreds. If this phenomenon occurs, induction of large current is often difficult in the repeater because the one peak characteristic of the induced current is no longer expected and the single peak of resonance splits into multiple peaks which shift the resonant frequency corresponding to the circuit parameters. In this paper, we approach this difficulty by applying an auxiliary circuit to the RIC-WPT system with the repeater. As a result, under the fixed operating frequency, the induced current in the repeater can be kept large even if the frequency splitting phenomenon occurs. The effectiveness of proposed system was supported by the simulation and experiment.
Effect of Rotor Bar Number on Performance of Five-Phase Induction Machine for Traction

The paper investigates the effect of the bar number on the performances of a five-phase squirrel-cage induction machine with fractional-slot tooth concentrated winding. With a same stator, five different rotor bar numbers are chosen and the rotor magnetic circuits are designed using an analytical approach. Then, a finite-element analysis is done under two different supply conditions: fundamental currents and third harmonic currents. Finally regarding the used winding specificities the possibility of second harmonic current supply is evaluated. The results are presented in a comparative way in order to determine the impact of rotor bar number on torque quality for the different supply modes.
Answer-focused and Position-aware Neural Question Generation

In this paper, we focus on the problem of question generation (QG). Recent neural network-based approaches employ the sequence-to-sequence model which takes an answer and its context as input and generates a relevant question as output. However, we observe two major issues with these approaches: (1) The generated interrogative words (or question words) do not match the answer type. (2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer. To address these two issues, we propose an answer-focused and position-aware neural question generation model. (1) By answer-focused, we mean that we explicitly model question word generation by incorporating the answer embedding, which can help generate an interrogative word matching the answer type. (2) By position-aware, we mean that we model the relative distance between the context words and the answer. Hence the model can be aware of the position of the context words when copying them to generate a question. We conduct extensive experiments to examine the effectiveness of our model. The experimental results show that our model significantly improves the baseline and outperforms the state-of-the-art system.
High Gain 2.4 GHz Two-Way Direction Wi-Fi Antenna for Underground Mine-Tunnel

This paper shows A 2.4GHz two-way directional Antenna of Wi-Fi Systems for Underground Mine-Tunnel. The designed antenna enhances wireless transmission of signals in an underground mine-tunnel in a two-way direction. The research was carried-out through scientific design and simulation-using high frequency structure simulator (HFSS). This antenna has fabricated and tested. The antenna is in the shape of a rectangle, partitioned into 12 sub-rectangular rings hooked together, that work in a standing-wave mode. The high-gain two-way radiation property realized by the currents distribution in-phase on the vertical edges. The dimensions of the desired ring are 230 mm, 22 mm and 0.8 mm $(1.84\lambda, 0.17\lambda $ and $ 0.006\lambda $ at 2.4 GHz). The simulated and measured -10-dB bandwidth is about 340 MHz (2.320–2.66 GHz), the simulated peak gain is 8.4 dBi. Presentation and discussion of the simulated results showed that the proposed antenna in two-way direction application is of high directional radiation gain, low profile and easy to build.