Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards ℓ1 Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty

Nowadays, numerous AI systems employ deep neural network models with excessive number of parameters to obtain superior performance on real-world applications. Such systems relies on high-performance GPUs to achieve real-time inference, especially for the ones that involve convolutional operations. This prohibits the practical deployment on resourcescarce edge devices. To reduce model size and inference time, model pruning for deep neural network models has been an active thread of research in the recent years. Among a significant amount of literature towards different model pruning strategies, ℓ1 regularization is commonly considered as a simple solution for sparse models. However, a fundamental issue is seldom addressed: "why ℓ1 regularization can be effective for model pruning". In this work, we provide a theoretical explanation showing that, for a specific type of neural network models with ℓ1 regularization, tasks of higher accuracy result in higher pruning ratio. Hence, network pruning could be exceptionally effective on high accuracy tasks. Based on the theoretical analysis, we demonstrate the effectiveness of ℓ1 regularization learning on two iconic computer vision tasks: (a) face detection and (b) image segmentation. The experiment results show that, with suitable ℓ1-regularized optimizations, even for a compact model like MobileNetV2, the model size can be reduced by an order of magnitude without significant losses on the accuracy. Furthermore, we show that with proper implementation of sparse convolution, the obtained sparse neural network models can achieve multiple times speed-ups not only in FLOPs, but also in actual inference time.
Extraction of small water body information based on Res2Net-Unet

As the extraction of small water bodies in remote sensing images has problems such as water line interruption and pretzel phenomenon, in order to be able to improve the extraction accuracy of small water bodies, this paper proposes a small water body extraction method based on Res2Net- Unet. The method uses the encoder and decoder structure of the UNet model. Firstly, the ResNet-50 network of the Res2Net module is used as an encoder, thus exploiting the feature information at multiple scales in the image. Secondly, a hybrid domain attention mechanism is incorporated into the decoder structure to fully mine the spatial and channel features in the image. Finally, a jump connection is added between the encoder and decoder to better fuse the features extracted by the encoder and decoder. Experiments on the Chinese Gaofen-1(GF-1) image datasets from two study areas show that the method in this paper is feasible for more complete and more accurate extraction of small water bodies compared with common deep learning models.
Perception and production of Mandarin vowels by teenagers–blind and sighted

This study aims to explore the perception and production of Mandarin vowels by the blind and sighted. Twenty blind and twenty sighted teenagers participated in the identification and pronunciation experiment of five Mandarin vowels. We calculated the variability and contrast parameters in the first and the second formant space to investigate the articulatory and perceptual abilities of subjects. The Coefficient of Variation (CV) used to quantify vowel variability reflected the precision of perception and production. The result demonstrated that blind people had more variable perceptions, but there was no difference in production. Vowel contrast was represented by the Average Vowel Space (AVS) of /a/, /i/and/u/, reflecting the distinguishability of vowels. The AVSs of the blind were significantly smaller in perception than the sighted, but larger in production. We found that the difference in perception mainly comes from the second formant. Also, the specific vowel absence observed in blind participants in the perceptual experiment suggested that the lack of vision is likely to bring about differences in vowel perception cues. Unfortunately, we did not find any correlations between perception and production in terms of precision and distinction. Improved experiments are required to explore the relationship between perception and production.
A Hybrid ConvLSTM Deep Neural Network for Noise Reduction and Data Augmentation for Prediction of Non-linear Dynamics of Streamflow

Long Short-Term Memory (LSTM) models are at the cutting edge of artificial learning and ecoinformatics in regards to water quantity prediction. However, one driver for more accuracy, efficient, and robust, water pollution perdition methods is climate change, and in particular global sea level rising. Statistical systems are no longer reliable and new prediction models need to be explored due to the increasing nonlinearity of streamflow predictors and extremes sea level changes. Another driver is that, in places with legacy infrastructure, updated water monitoring systems and unreliable forecasting framework, state-of-the-art LSTM -based models suffer due to the presence of noisy data. This paper proposes multiple LSTM-based models with Scharr filtering to improve the streamflow prediction accuracy against noise. A hybrid ConvLSTM approach is realized to overcome the nonlinearity of the main predictors and the noises. The evaluation results demonstrate that the proposed hybrid ConvLSTM model can effectively improve the overall prediction accuracy for both real-world data and the noise-augmented data. The hybrid ConvLSTM model also obtained competitive and even better performance compared with several state-of-the-art methods. In addition, our proposed design achieves comparable performance in terms of prediction time.
Colocated MIMO Radar Waveform Design with Raised Cosine Filter

In colocated Multiple-Input Multiple-Output (MIMO) radars, phase coded waveform design is an important issue and in real applications, a raised cosine filter is typically used to avoid frequency-domain signal mutual interference, which however is rarely considered in existing waveform design algorithms. In this paper, we design MIMO radar transmit waveforms with optimized raised cosine filters to advance real application of MIMO radar waveforms. A waveform optimization criterion is formulated with the filter, which aims at suppressing auto and mutual correlation sidelobes and the mismatch to a desired transmit beampattern. Numerical results with transmit beampattern with two equal peaks are presented, indicating that waveforms with good sidelobes and close to the desired transmit beampattern can be achieved even with this filter. The Doppler sensitivity of this waveform is also studied.
Denoising of UAV Micro-Doppler Signatures Based on BEMD and Wavelet Transform

In recent years, the detection and classification of UAVs has become a hot issue. The difficulty is that UAVs have very small RCS and usually fly at low altitudes. Radar echo contains strong noise, clutter and other interference. To solve this problem, this paper proposes a denoising algorithm combining bivariate empirical mode decomposition(BEMD) and wavelet transform based on UAV micro-Doppler characteristics. First, the pre-processed radar signal is decomposed into complex modal components by BEMD decomposition. Then the peak value of the autocorrelation function is used to automatically select the IMF components containing the micro-Doppler feature to perform wavelet threshold denoising, and finally the micro-Doppler signal is reconstructed. Experiments were carried out using simulation and measured data of UAV rotor blades. Experimental results show that the proposed method has a significant denoising effect at low Signal-Noise-Ratio(SNR).
A Probabilistic Jamming Strategy Model for Frequency Agility Radar Anti-Jamming Problem

With the development of electronic warfare, the jammer is much smarter than before and can adopt different strategies to jam the radar. To design efficient anti-jamming strategies, the radar anti-jamming problem is recently modeled as a Markov decision process (MDP) problem and reinforcement learning (RL) algorithms are used to solve it. However, common RL algorithms needs a large number of interaction samples, which is not realistic in practice. To address this problem, we propose a probabilistic jamming strategy model for the frequency agility radar (FA) that can learn the unknown jamming strategy through fewer interaction samples. Based on proposed model, a jamming strategy is first expressed by a series of unknown probability matrixes and weights. Then, the FA radar is used to induce jammer to emit jamming signals and collects samples of radar-jammer interaction in MDP. The model parameters are estimated by means of maximum likelihood estimation (MLE). Finally, RL algorithm can be used to train the anti-jamming strategies through offline interaction. Simulation results illustrate the effectiveness of the proposed model.
A Clustering Method for Assisting in Tagging Same-Behavior Tested Web Elements

Automated test case generation for web testing has been widely recognized as a difficult task. Even nowadays, web testing still heavily relies on manual efforts for creating test cases. With the rapid development and innovation of web technologies, web applications become more and more complicated, which imposes a heavy burden on testing a large number of interactable web elements. However, despite there are a considerable number of elements on a webpage, many elements are often visually arranged as groups. In some cases, elements within the same group perform the same action (i.e., triggering the same server-side scripts), which means testing on only one element for a group is enough regarding test coverage. In this paper, we present a method for automatically clustering web elements into groups. By applying this method, given a few tested elements, some of the other elements will be automatically identified and highlighted as a group for that element, and then a web tester can tag those elements as ‘tested’ at once if the tester decides they will not contribute to test coverage. In the experimental evaluation, several case studies are presented to illustrate the effectiveness of the method.
Generalized Non-Iterative Time Delay Estimation Based on Taylor Interpolation Model

Time delay estimation (TDE) has been extensively studied in the past for off-grid cases since the time delay of the target does not fall on the sampling point (on-grid) usually. In this paper, two generalized non-iterative time delay estimation methods are proposed. The maximum range cell sampling point of signal powers after pulse compression is used as a rough estimate of the time delay, while multiple points around the peak location are employed to obtain the fine estimate. Based on the Taylor expansion model, the explicit generalized expressions of two methods for the fine estimate of the time delay are derived without the knowledge of the closed-form signal expression. The proposed methods can be applied in applications where the linear frequency modulation (LFM) signal and the nonlinear frequency modulation (NLFM) signal (without closed-form expression) are employed, and only the sampling values of several points around the peak location are required. Compared with existing methods, improved estimation performance can be achieved by both methods.
Robust Waveform and Filter Bank Design of Polarimetric Radar under Local Waveform Constraints

Joint design of transmit signal and receive filter bank for polarimetric radar with local waveform constraints is considered. Assuming Target Aspect Angle (TAA) unknown but belong to an interval, we consider the worst-case Signal to Interference plus Noise Ratio (SINR) at the output of the filter bank as the figure of merit to optimize transmit waveform in horizontal and vertical polarization channels, respectively, subject to energy and similarity constraints. An iterative optimization procedure that effectively solves the non-convex optimization problem is developed. The proposed method is validated through experimental results and compared with existing global design, thus showing its attractiveness.
Hybrid Model Based on Isolated Forest and WT-Catboost for Wind Power Forecasting

A hybrid model based on isolated forests (IF), wavelet transform (WT), and categorical boosting (CatBoost) is proposed for wind energy prediction. In this prediction method, IF is first utilized to catch outliers in the raw wind power time series. Afterward, WT is applied to decompose the wind power sequence processed by outlier detection into several frequencies with more pleasing silhouettes. Finally, CatBoost is employed to learn the nonlinear attributes of these frequencies. The hybrid model is demonstrated on actual wind farm operation data and shows its effectiveness compared with other methods.
A Speech-Noise-Equilibrium Loss Function for Deep Learning-Based Speech Enhancement

The deep learning (DL)-based speech enhancement (SE) has demonstrated its advantage over the classical methods. In most DL-based SEs, however, the systems are optimized based on the minimum mean squared error (MSE), which could result in poor performance in severe noise conditions, e.g., very low signal-to-noise ratios. This paper presents a speech-noise-equilibrium loss function, i.e., a weighted combination of the speech distortion and the noise residue, for network training. Furthermore, based on the observation of the non-Gaussian distribution of the prediction error, a mean absolute error (MAE) criterion is adopted for speech distortion, and hybrid training, i.e., MSE followed by MAE, is proposed for network optimization. Experiment results demonstrate that long-short term memory networks (LSTM)-based SE systems with the proposed loss function achieve better performance than the baselines, particularly, improving both speech quality and intelligibility at low signal-to-noise ratios.
A 10.12μW 101.98dB-SNDR Three-step Incremental Analog-to-Digital Converter

A three-step incremental analog to digital converter (IADC) is presented in this paper. This structure only needs two amplifiers and can extend the performance of a 2nd-order IADC close to that of a 4th-order IADC. The resolution of the IADC is extended by configuring it as a 1st-order IADC and a single-slope ADC respectively, in two extra steps. This ADC uses inverter-based amps to improve the power efficiency instead of power-hungry amps. The simulation shows that it consumeS 10. 12μW in a 1.8V supply with a clock of 250kHz. It obtains an SNDR of 101.98dB and a bandwidth of 488Hz, which results in an excellent Schreier figure of merit (FoM) of 178.8dB.
Optimal Trained Deep Maxout Model for Intrusion Detection in Cloud

The decentralized and distributed nature of cloud computing makes it easier for diverse society sectors, including environment, business, and government, to accept and use it. As it permeates every element of society, this computing paradigm is open to attacks and intrusions. Both security and privacy are seriously jeopardized by the enormous amount of data stored on the cloud. Along with this increase in usage, security risks to networks, the Internet, websites, and businesses are also rising. In a massive data environment like this, it can be challenging to find intrusions. Numerous artificial intelligence (AI) or machine learning-based intrusion-detection systems (IDSs) have been proposed for various forms of network assaults, but the majority of these systems are either unable to recognize unknown attacks or are unable to react to such attacks in real time. We suggested a new Deep Maxout algorithm-based intrusion detection system. Preprocessing, feature extraction, and detection are the three processes used in this instance of intrusion detection. The z-score normalization method is applied to normalize the input data during the preprocessing stage. Following that, the feature extraction stage will use the normalized data. There are two features extracted that are statistical features and higher-order statistical features (HOS). The statistical features are defined in terms of the median, standard deviation, minimum, maximum, entropy, and variance. HOS characteristics are skewness and kurtosis. An optimized Deep Maxout model is used for the detection process, where the training will be carried out by the COOT optimization algorithm. This assures the improvement of detection accuracy.
Fuzzy based Pollution Severity Prediction in Polymer Insulator using Harmonic Analyzer

Comparing the composite polymeric insulator (CPI) to glass and porcelain insulators, additional benefits are identified. Even though it is deemed to be affected by the taint from the environment. Leakage current is present in almost every high voltage apparatus including insulators, bushings, cables and lines even in clean and polluted conditions. As the pollution level rises, the harmonics caused by the leakage current become more pronounced, which is now the challenge faced in many of the insulator maintenance. This condition on the CPI can be used in this study to assess how contaminated the insulators are above them, which over time can cause breakage, flashover, and even transmission line failure. The odd harmonic component, such as the third, fifth, seventh, and ninth along with the breakdown voltage of the insulator is used as an indication, and the value of the ratio between them is fed to the fuzzy network. This computation model allows the prediction of an accurate pollution level on the insulators.
A Convolutional Neural Network for Heterogeneous Ship Images Classification

In this work, heterogeneous ship images classification has been implemented by using Deep Learning techniques. The main goal of our study is to build a classification model that performs well on data collected from two different sensors i.e. optical sensor and radar sensor. We have proposed a binary classification solution. Our goal was to separate the ship images from the others. We selected a convolutional neural network (CNN) as our classification method and SoftMax as the prediction method. A CNN model was trained from scratch during which we obtained an accuracy score of 97.16%. We also used techniques such as transfer learning and fine-tuning to improve the previous accuracy. We finally obtained 99.30% as training accuracy. During the testing phase, 93.06% was recorded as the best performance.
CNN-Based SAR Automatic Target Recognition Using SAR Raw Data

Automatic target recognition (ATR) for synthetic aperture radar (SAR) has been a significant research topic for decades. Existing methods of ATR perform the task after image formation. However, the imaging process of target information may lose the target abstract feature information hidden in the original data. Motivated by this, we feed the SAR raw data to a deep convolutional neural network (CNN) that does not require image reconstruction. To evaluate the method, we trained two well-known CNN networks, VGG19 and ResNet18, on the simulated dataset and MSTAR dataset. The results show that both CNN network architectures can achieve good results for classification.
Analysis of Kuramoto models for AC microgrids based on droop control

As a typical nonlinear coupled system, microgrid has nonlinear coupling relationships between generations and distributions which cannot be ignored. In this paper, the problem of power balance between distributed generators and loads is transformed into the motion synchronization problem of multiple Kuramoto coupled oscillators. And this paper proposes to model an islanded microgrid by using a second-order Kuramoto model. By exploiting the model, our key sight is to address the transient stability problem and find a trapping region expressed explicitly in the parameters. Finally, the similarity between transient stability of islanded microgrid and frequency synchronization of Kuramoto coupled oscillators is analyzed, and the validity of microgrid Kuramoto models is verified based on the 3-generator 5-node islanded microgrid MATLAB/ Simulink simulation platform.
Interminable Flows: A Generic, Joint, Customizable Resiliency Model for Big-Data Streaming Platforms

The examiner of cloud computing systems in the last few years observes that there is a trend of the emergence of a new Big Data framework almost every year. Since Hadoop was developed in 2007, new frameworks followed it such as Spark, Storm, Heron, Apex, Flink, Samza, Kafka.etc. Each framework is developed in a certain way to target and achieve certain objectives better than other frameworks do. However, there are few common functionalities and aspects that are shared between these frameworks. One vital aspect all these frameworks strive to achieve is better reliability and faster recovery time in case of failures. This is particularly crucial for streaming systems (compared to batch processing systems) where events are processed and monitored online in real time, and any delay in data delivery will cause a major inconvenience to the users. Another observation is that some reliability implementations are redundant between different frameworks. Encapsulating these implementations into one layer and make it shared between different applications will benefit more than one framework without the burden of re-implementing the same reliability approach in each single framework. These observations motivated us to present Warden, a generic, multi-framework, flexible, customizable, low overhead protocol to ensure the resiliency of streaming applications running on streaming Big Data frameworks. Most reliability protocols carry out one rigid fault tolerance approach targeted towards one system at a time. It is more challenging to provide a reliability approach that is pluggable in multiple Big Data frameworks at a time and can achieve low overheads comparable with single targeted framework approaches, yet is flexible and customizable by its users to make it tailored towards their objectives. The genericity is attained by providing an interface that can be used in different applications from different frameworks. The low overhead is achieved by providing faster application finish times with and without failures. The customizability is fulfilled by providing the users the options to choose between two delivery semantics (Exactly Once / At Most Once) combined with two fault tolerance guarantees (Crash Failures / Byzantine Failures). To the best of our knowledge, such approach was never tried on multiple streaming frameworks before. We built a prototype of Warden on Flink and Samza (with Kafka) streaming frameworks. Our evaluations highlight the effectiveness of our approach in the presence of failures and without failures compared to other fault tolerance techniques (such as checkpointing).