Reviewerly/exHarmony huggingface dataset:
Target Level Change Detection in Multi-temporal SAR Images Based on Prior Position

Change detection of target level in SAR images is an interesting topic, which can be used in many important applications. In view of the image processing problem of target change detection, especially the practical problem of analyzing the target change information quickly in multitemporal spaceborne SAR images, this paper puts forward a method to detect the SAR image targets quickly in different periods based on the prior information of target location. In this method, a large number of SAR images are observed to analyze the probable positions of the targets. In the test images, we only need to detect targets and detect the change of targets in prior positions. This paper analyzes the increase, decrease, and relative position change of the target in the next time relative to the previous time from the perspective of quantity and position. Compared with the true value of real images change detection, this method can achieve 89.6% accuracy.
Data-Driven Usage Profiling and Anomaly Detection in Support of Sustainable Machining Processes

There is a lot of room for improvement towards more sustainability in manufacturing companies. During the machining operations, replacement of the cutting tools is not done in an optimal way, resulting in sub-optimal usage of resources and inefficiencies during the production process. Using data-driven approaches to extend the usage of tools can greatly improve on this shortcoming by optimizing the replacement process of these tools. This study is therefore sought to investigate the value of several data-driven approaches, applied to an industrial dataset, to achieve this goal. Although the examined data-driven methods were applied to a dataset which has been generated under a wide variety of machining conditions and lacks reliable ground truth, the obtained experimental results confirm that these methods are indeed capable of extracting informative profiles from the tool usages and can identify anomalous patterns and signs in the time-series datasets collected during different machining processes.
Throughput-Optimized Implementation of Isogeny-based Cryptography on Vectorized ARM SVE Processor

We present the first implementation of Supersingular Isogeny Key Encapsulation (SIKE) using ARM's Scalable Vector Extension (SVE), aiming to maximize throughput. Large integer multiplications in SIKE can be accelerated by using 64-bit integer multiplication instructions available in SVE, which was not available in previous work. The main problem is at addition with carry: SVE does not natively support add-with-carry or reduced radix. We propose to handle carries using predicate instructions. As a result, 3.4 times higher throughput was obtained on Wisteria/BDEC-01 (Odyssey) powered by Fujitsu A64FX CPU compared with SIDHv3.5, an ARMv8 assembly implementation of SIKE developed by Microsoft.
Implementation Combination of Case-Based Reasoning and Rule-Based Reasoning for Diagnosis of Herpes Disease

The skin is the outermost part of the body that is sensitive to external stimuli. One of the skin diseases that can be transmitted is herpes. Belated in understanding herpes skin disease can be fatal to sufferers because herpes disease that is too late to be treated can affect more serious diseases. Therefore, we need a mobile-based application that can identify the infectious skin disease herpes. An expert system is one of the fields of science that can solve a problem in a system. The expert system has several methods that can be used, namely case-based reasoning (CBR) and rule-based reasoning (RBR) methods. CBR can be used to solve a new problem by utilizing previous problems and RBR is a method that can be used to solve a problem using a rule base, where each rule is obtained from experts and literature studies. By applying the combination method of CBR and RBR which can be used to diagnose herpes skin disease, it is hoped that it will prevent the spread of herpes disease from spreading.
Signal structure information-based data association for maneuvering targets with a convolutional Siamese network

Traditional data association methods usually suffer performance degradation caused by model mismatch when target maneuvers occur. In addition, the signal structure information (SSI) available from the employed signal processing techniques is rarely used in data association due to the lack of appropriate tools. To overcome the model mismatch problem brought by the target maneuvers, we resort to a data driven method to design the data association algorithm. To make full use of the SSI, a convolutional neural network is used to transform the SSI into a feature space, where the similarity of the SSI between two observations can be calculated using the Euclidean distance. The resulting structure is the convolutional Siamese network (CSN). By comparing the similarity with a predetermined margin parameter, the decision about association can be made. Simulation results are presented to show that the use of SSI in the designed CSN can improve the association probability of maneuvering targets.
Repeatable Pattern Mining for Accurate Subtraction of Backgrounds with Waving Objects in Underwater Videos

The success of advanced Background Subtraction (BGS) algorithms for dynamic backgrounds is mostly in land scenes such as those in CDNet benchmarks; few handle underwater scenes, since existing underwater video datasets are either in low resolution or with only static backgrounds. Consequently, the lack of reliable BGS support makes supervised Moving-Objects Segmentation (MOS) algorithms much harder to adapt to unknown underwater scenes because of the diversities of the aquatic environments. For example, those trained by the latest underwater image dataset, SUIM, are ineffective in the underwater videos of our experiments.The underwater waving objects (e.g., plants) often render existing BGS algorithms inaccurate due to three types of errors: (a) incompletely identified MOs (Moving Objects), (b) missing MOs, and (c) falsely identified MOs. In this paper, we propose a novel Clustering-Based Multi-State Background Representation (CBMSBR) model to learn and represent the repeatable patterns of waving movements in k background states (i.e., color ranges) per pixel, and thus accurately subtract the background waving objects to reduce these errors. In addition, we further develop a CBMSBR+ model to remove the more challenging background objects in unusually large magnitudes of wavings. Both models come from a basic observation: the video pixels in the waving zones repeatedly switch among multiple background states; e.g., a pixel switches among water state, plant 1 state, and plant 2 state. To test our proposed models, we create experiments using three types of challenging scenarios that each often covers at least two error types, i.e., the scattered MOs scenario covering (b) and (c), the crowded MOs scenario covering (a) - (c), and the slow MOs scenario covering (a) and (c). Experiments on these scenarios demonstrate the accuracy, effectiveness, and efficiency of our models and their applications in MOS improvements.
A Side Information Enhanced Matrix Factorization Approach via Hierarchical Generalized Linear Model

Matrix factorization (MF) is a popular method for collaborative filtering. Recently, more and more MF methods have been proposed to incorporate side information. However, most of them are vulnerable to changes in data or sub-models. Moreover, data often follows a Pareto distribution and such an imbalance of data leads to a biased global mean, affecting the prediction accuracy. To overcome these defects, we designed a Hierarchical Generalized Linear Model-based MF method (HGLMMF) which can leverage both the original and processed side information. More specifically, HGLMMF utilizes one portion of the side information to construct covariates for fixed effects and the other portion to model the cluster-specific effects to adjust the global-bias problem. In fact, a number of state-of-the-art MF models can be viewed as special cases of HGLMMF. The obtained prediction results from experiments prove that HGLMMF is highly competitive with state-of-the-art methods.
Centralized Information System for Data Services of the Pekanbaru High Court Decisions

The progress of information technology is currently very rapid, so it forms a way of life that is widely influenced by electronics. It can improve the performance of the company or agency. The performance of the Pekanbaru High Court in the information service desk was not optimal due to routine data collection, so there was no comprehensive information on how the performance of the Pekanbaru High Court had improved, such as services satisfaction reports. It was a serious problem because the Supreme Court would assess the Information Desk at the High Court in its jurisdiction at any time. The solution to this issue was to develop a centralized information system for web-based Pekanbaru High Court decision data services that could support the performance and services of the Pekanbaru High Court. The method used the Prototype method using Unified Modeling Language (UML) tools. This research used 9,344 data records. The system carried out several tests, namely black box testing with five devices with a mean success rate of 100%. In addition, the results of the User Acceptance Test (UAT) test obtained the interpretation criteria Strongly agree with the range of 81 percent up to 100 percent and met the user’s functionality requirements.
Joint Debiased Representation Learning and Imbalanced Data Clustering

One of the most promising approaches for unsu-pervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn representation using deep neural networks and perform clustering by defining a clustering loss on top of embedded features. However, these approaches are sensitive to imbalanced data and out-of-distribution samples. As a consequence, these methods optimize clustering by pushing data close to randomly initialized cluster centers. This is problematic when the number of instances varies largely in different classes or a cluster with few samples has less chance to be assigned a good centroid. To overcome these limitations, we introduce a new unsupervised framework for joint debiased representation learning and image clustering. We simultaneously train two deep learning models, a deep representation network that captures the data distribution, and a deep clustering network that learns embedded features and performs clustering. Specifically, the clustering network and learning representation network both take advantage of our proposed statistics pooling block that represents mean, variance, and cardinality to handle the out-of-distribution samples and class imbalance. Our experiments show that using these repre-sentations, one can considerably improve results on imbalanced image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to the out-of-distribution dataset.
Automatic Modulation Recognition for Radar Signals Based on ACSE Networks

Automatic modulation recognition for radar signals plays a significant role in electronic warfare. Conventional recognition methods may suffer from the recognition accuracy and the computation complexity under low signal-to-noise ratio (SNR). In this paper, a novel method based on Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks and a fusion strategy is proposed to recognize seven kinds of radar signals automatically. First, features in these three domains are converted into images. Then the images are as inputs for ACSE networks which can extract and learn deep features without complex data pre-processing. Finally, inference results in the three domains are fused using voting strategy of ensemble learning to output the final inference result. Via simulations, the robustness and effectiveness of voting strategy are proved. The results on simulation signals show that the proposed method can achieve more than 95% accuracy at −6dB. Compared with SVM method and three typical neural networks, the ACSE networks owns better recognition performance especially under low SNRs. The results on measured signals also demonstrate that the proposed method outperforms other methods.
AdaptiveFormer: A Few-shot Speaker Adaptive Speech Synthesis Model based on FastSpeech2

In recent years, adaptive text-to-speech (TTS) has attracted increasing interests in speech community, which aims at synthesizing a new speaker’s voice with few data. However, the synthesized speech of adaptive TTS model is still facing the challenge of performance degradation, because it is difficult to learn unseen speaker style with limited data. To tackle this problem, we propose a new adaptive TTS model, called AdaptiveFormer (AF), which generates natural speech with high similarity for unseen speaker. We use two methods to improve the quality of synthesized speech. 1) We propose a novel adaptive attention mechanism to replace the traditional multi-head self attention mechanism. 2) We propose a Conditional Predictor to predict the valuable rhythm information, including duration, pitch and energy, which is a Variance Adaptor [1] based architecture. We use the Conditional Layer Normalization (CLN) to enhance the accuracy of predicted rhythm information, and improve the speaker adaptation for unseen speaker. According to the MOS and SMOS results, the AF model outperforms the baseline (spk emb) [1] approach on the unseen emotional corpus.
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition

The Transformer architecture model, based on self-attention and multi-head attention, has achieved remarkable success in offline end-to-end Automatic Speech Recognition (ASR). However, self-attention and multi-head attention cannot be easily applied for streaming or online ASR. For self-attention in Transformer ASR, the softmax normalization function-based attention mechanism makes it impossible to highlight important speech information. For multi-head attention in Transformer ASR, it is not easy to model monotonic alignments in different heads. To overcome these two limits, we integrate sparse attention and monotonic attention into Transformer-based ASR. The sparse mechanism introduces a learned sparsity scheme to enable each self-attention structure to fit the corresponding head better. The monotonic attention deploys regularization to prune redundant heads for the multi-head attention structure. The experiments show that our method can effectively improve the attention mechanism on widely used benchmarks of speech recognition.
Deep Knowledge Tracing with Learning Curves

Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior performance over traditional approaches. However, they ignore the explicit modeling of the learning curve theory, which generally says that more practices on the same knowledge concept enhance one's mastery level of the concept. Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model to enable learning curve modeling. In particular, when predicting a student's response to the next question associated with a specific knowledge concept, CAKT uses a module built with three-dimensional convolutional neural networks to learn the student's recent experience on that concept. Moreover, CAKT employs LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional module. As such, CAKT can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. Experimental results on four real-life datasets show that CAKT achieves better performance compared to existing deep KT models.
G-SM-CAM: A Fast Visual Understanding of CNNs in SAR Images Interpretation

Numerous automatic recognition methods based on convolutional neural networks (CNNs) can achieve high calcu-lation efficiency owing to its end-to-end structure. However, the internal mechanism of CNNs is intransparent which is limiting or even disqualifying in SAR image interpretation. To provide a visual understanding of CNNs' mechanism, we propose a Group-Self-Matching class activation mapping (G-SM-CAM) inspired by the split strategy and Self-Matching CAM. In specific, feature maps are firstly “self-matched” with the input image as the renewed feature maps. Then these renewed feature maps are split into several groups. Finally the saliency map can be generated by these sub-feature maps. G-SM-CAM is efficient and effective on SAR images, which runs dramatically faster than Self-Matching CAM at minor cost of saliency map quality. Numerous experimental results demonstrate the validity and efficiency of G-SM-CAM based on a benchmark dataset MSTAR.
Cantonese neural speech synthesis from found newscasting video data and its speaker adaptation

This paper investigates the speech synthesis of a Chinese dialect: Cantonese. Without a sizable publicly accessible speech corpus, there are relatively few Text-to-Speech (TTS) research works on this spoken language. In this paper, we present a data mining pipeline to collect large-quantity and high-quality Cantonese audio data from a newscasting video program. The found data enables the training of a deep learning-based multi-speaker speech synthesis model with merely Cantonese audio. We varied the amount of data for speaker adaptation from a pre-trained model. We suggest that fine-tuning the model with 2 audio hours gives a similar synthetic audio quality, in the aspects of voice similarity and mel cepstral distortion (MCD) to groundtruth audio, to that from more audio hours. Furthermore, we conducted a subjective preference test on the synthesized speech samples from the adapted model against some speech samples generated through Microsoft’s cloud-based TTS service. Our samples are more preferred by reviewers in terms of naturalness when uttering texts that are about daily conversation and from novel books.
A New Bias Pseudo-Measurement Method for Distributed Sensor Bias Estimation

To deal with the problem of sensor registration in distributed sensors, a common method is to construct pseudo-measurements only related to the bias vector, and then estimate the bias through these pseudo-measurements. Consider the scene of limited communication, a new method to construct the bias pseudo-measurement only by operating the local tracks, covariances, and the equivalent bias measurement matrices is proposed in this paper. Compared with the existing methods, the proposed method can save about 36.4% of the communication cost with other performance unchanged in the system exemplified in this paper.
Dialogue scenario classification based on social factors

The tendency of interlocutors to become more similar to each other in the way they speak, this behavior is known in the literature as entrainment, accommodation, or adaptation. Previous studies indicated that entrainment can be treated as a social factor in human-human conversations. However, previous research suggests that this phenomenon has many subtleties. One of these cues is that entrainment on an acoustic feature might be associated with disentrainment on another in conversation, which means we have to consider these features together. Therefore, we proposed a linear dimensionality-reduction method that combines acoustic features to calculate three entrainment metrics: proximity, convergence, and synchrony. The three entrainment metrics are referred to as social factors hereafter. Our results show these social factors play an important role in a classification task. We also found that these social factors perform a better classification accuracy than combining each individual acoustic feature’s entrainment. The proposed social factors can help the human-machine interface to have the ability to adapt to the different scenarios in dialogue.
CE-GAN : A Camera Image Enhancement Generative Adversarial Network for Autonomous Driving

Cameras onboard autonomous, as a critial component of the sensor system of automatic driving, plays a vital role in perception of driving and road environment. However, in some bad weather or unpredictable situations, the image quality obtained by the in-vehicle sensing camera is not ideal, which will become an extremely unsafe factor for autonomous driving. In order to improve the safety of self-driving vehicles, we proposed a novel high-quality image of invehicle cameras generation approach CE-GAN, a conditional generative adversarial network that attempt to leverage the point cloud data from on-board lidar to compensate the defect of visible image to improve the image quality of on-board cameras. Inspired by the generative adversarial networks, our method establishes an adversarial game between the generator and the discriminator We designed specifically loss function for different reasons for image quality impairment including partially obscured and fogged. Consequently, extensive experiments show that CE-GAN renders better performance in detail texture, compared with conventional Cycle-GAN, pix2pix methods without assistance of LiDAR data.
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition

Recently, Conformer based CTC/AED model has become a mainstream architecture for ASR. In this paper, based on our prior work, we identify and integrate several approaches to achieve further improvements for ASR tasks, which we denote as multi-loss, multi-path and multi-level, summarized as ”3M” model. Specifically, multi-loss refers to the joint CTC/AED loss and multi-path denotes the Mixture-of-Experts(MoE) architecture which can effectively increase the model capacity without remarkably increasing computation cost. Multi-level means that we introduce auxiliary loss at multiple level of a deep model to help training. We evaluate our proposed method on the public WenetSpeech dataset and experimental results show that the proposed method provides 12.2% ~ 17.6% relative CER improvement over the baseline model trained by Wenet toolkit. On our large scale dataset of 150k hours corpus, the 3M model has also shown obvious superiority over the baseline Conformer model. Code is publicly available at https://github.com/tencentailab/3m-asr.