Reviewerly/exHarmony huggingface dataset:
LIGHT-DCSFN: A Light Cross-scale Fusion Network for Single Image Deahzing

At present, the image dehazing algorithm based on deep learning has many problems such as multiple model parameters and taking a long time for single image dehazing. Based on this, a fast image dehazing method based on depth cross scale fusion network is proposed. First, depth separable convolution is used to replace the normal standard convolution in the original network to improve single image dehazing efficiency. Secondly, a color feature extraction module is added on the basis of the original network to make the improved network better for image dehazing. Finally, this paper uses the network to predict the haze concentration map of the input image instead of directly obtaining the haze-free image through the network, so that the final image predicted by the network is clearer and more natural. The results indicate that the proposed algorithm greatly improves the dehazing time of a single image and can dehazing in most scenes.
Machine Learning Based End-to-End Constellation Training for Communication Systems

The constellation design is critical to the performance of communication systems. Conventional constellation schemes usually do not achieve optimal performance. Their design is usually based on some assumptions such as channel models and equal information bit probability distributions. Therefore, the performance degrades if the assumptions are unreal or oversimplified. Furthermore, effective and efficient methods to generate massive order constellations are still missing. In this paper, we propose a machine learning based constellation training method for communication systems. We use the autoencoder to train the constellation points. The proposed method trains the constellation by transmitted and received information bits. It does not depend on the assumptions such as specific channel models or information bit probability distributions. The proposed method can effectively optimize the constellation points, even for massive order constellations. Simulation results show that the proposed method outperforms conventional methods. The performance improvement is more significant when the number of bits per symbol is larger.
Defense Against Adversarial Examples Using Beneficial Noise

The state-of-the-art techniques create adversarial examples with a very low-intensity noise making the detection very hard. In the proposed work, we explore adding extra noise and filtering operations to differentiate between benign and adversarial examples. We hypothesize that adding lightweight noise affects the classification probability of adversarial examples more than benign ones. The proposed architecture uses them as features to train a binary classifier and detect adversarial examples in high-resolution, real-world images. Specifically, we look at beneficial noise generated through targeted adversarial attacks and noise from JPEG compression to perturb adversarial examples. Our standard classifier was able to distinguish benign and adversarial for BIM, PGD, and DeepFool in, approximately, 96.5%, 97%, and 85% of the cases, respectively, on high-resolution images from the ImageNet dataset.
Image Block Cipher Based on Parameterized Cosine Transform over Finite Fields

This paper proposes an image block cipher scheme based on a parameterized finite-field cosine transform (FFCT) and a nonlinear feedback of permutation polynomial over finite field. First, a plain image was mapped from the integer field to a finite field and divided into non-overlapping <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8\times 8$</tex> subblocks. Subsequently, a parameterized FFCT was conducted for each block. Next, the nonlinearity in the cryptosystem was introduced by a feedback branch that received the FFCT output block, passed it through a nonlinear permutation polynomial mapping after delaying one block and added the result to the incoming plain image block. The initial FFCT output state before the first block was generated by a two-dimensional (2-D) logistic chaotic system. Finally, the transformed subblocks were merged into a single image as the ciphertext. The control parameters of the 2-D logistic chaotic system, the permutation polynomial, and the FFCT were used as cipher keys. The inherent nonlinearity of the permutation polynomial ensures the nonlinearity of the proposed cryptosystem. The modular operation of the FFCT and the permutation polynomial mapping over the finite field make the computation more efficient and ensure that the entire encryption and decryption system is strictly lossless one. Extensive experimental results show that the proposed image block cipher is feasible, efficient, and resist common classical attacks.
Correlation Loss for MOS Prediction of Synthetic Speech

For the speech mean opinion score (MOS) prediction task, many deep-learning-based methods are developed. Generally, system-level and utterance-level mean squared error (MSE), Linear Correlation Coefficient (LCC), Spearman Rank Correlation Coefficient (SRCC), and Kendall Tau Rank Correlation (KTAU) are leveraged as the evaluation metrics. However, we find that the objective functions for many MOS prediction networks are MAE or MSE based without an explicit correlation objective part. This paper investigates different correlation losses for voice MOS prediction networks. Based on the datasets and SSL-MOS baseline system provided by VoiceMOsChallenge 2022, we employ different auxiliary correlation losses to train the MOS prediction network. The experiment results show that the suggested auxiliary correlation losses increase the performance of the SSL-MOS network on the six correlation metrics. Compared with the two best-performing systems in the VoiceMOsChallenge 2022, our approach achieves close performance on the system-level correlation metrics with simpler system architecture.
Fast Image Restoration Using A Simplified Matrix-Type Recurrent Neural Network

Recently, a matrix-type recurrent neural network with two-layers structure has been successfully applied for the matrix-variable optimization model of image restoration, since it can effectively strengthen the computation speed of vector-variable optimization methods. This paper employs a simplified matrix-type neural network with one-layer structure to further improve the computational performance. First, a matrix-variable regularization optimization model is considered for image detail perseveration. Then a simplified matrix-type neural network is introduced for fast dealing with matrix-variable regularization optimization model. Finally, illustrative examples show that the simplified matrix-type recurrent neural network-based algorithm is more effective than the existing matrix-type neural network with two-layers structures in terms of computational performance, and has faster speed than the vector-type neural networks.
A Study of Review Hot Words Extraction Technology Based on the LSTM Web Review Validity Model

The huge amount of online review text data brings a great challenge to the extraction of valid information and hot words extraction work. This paper addresses this problem and designs a study on hot words extraction based on a Bidirectional LSTM(Long short term memory) online review text validity model. Firstly, data pre-processing is performed on the data set of online review texts collected by crawlers, secondly, a validity model of online review texts based on LSTM neural network is established to filter the valid online review texts, and finally, hot words are extracted from the valid review texts to get the hot words containing valuable information. In this paper, we take hotel review text as an example to conduct experiments, and the experimental results prove that the accuracy of LSTM online review text validity model reaches 90%, the loss value reaches 0.2, and the screening of valid text for hot words extraction achieves good results.
StyleGAN Encoder-Based Attack for Block Scrambled Face Images

In this paper, we propose an attack method to block scrambled face images, particularly Encryption-then-Compression (EtC) applied images by utilizing the existing powerful StyleGAN encoder and decoder for the first time. Instead of reconstructing identical images as plain ones from encrypted images, we focus on recovering styles that can reveal identifiable information from the encrypted images. The proposed method trains an encoder by using plain and encrypted image pairs with a particular training strategy. While state-of-the-art attack methods cannot recover any perceptual information from EtC images, the proposed method discloses personally identifiable information such as hair color, skin color, eyeglasses, gender, etc. Experiments were carried out on the CelebA dataset, and results show that reconstructed images have some perceptual similarities compared to plain images.
Domain Adaptation and Language Conditioning to Improve Phonetic Posteriorgram Based Cross-Lingual Voice Conversion

In this work, we examine two methods for im-proving phonetic posteriorgram (PPG) based cross-lingual voice conversion (CLV C). Previous research usually utilized a speaker encoder to characterize speakers' identity; however, the speaker embedding learned by the previous model tends to be language- dependent, degrading the performance of converted speeches. Therefore, we propose using the technique of domain-adversarial training. With this approach, the speaker embedding in different languages can be adapted into the same distribution to form a language-independent speaker embedding space. The other approach we propose is to employ external language conditioning to support our model to disentangle the language information from the speaker embedding. In our experiments, both methods are evaluated on a Japanese-English bilingual database. Besides subjective evaluation, two automatic objective assessment systems are adopted to assess the quality and speaker similarity of converted utterances. According to the experimental results, the two proposed methods can generate speaker embedding with reduced language dependency and improve the naturalness and speaker similarity of converted speeches.
Future Object Localization in Autonomous Driving Using Ego-Centric Images and Motions

In autonomous driving, future object localization (FOL) is actively used for trajectory prediction and a collision avoidance system. However, it is a challenging task to accurately determine the future locations of nearby pedestrians and vehicles during driving. In this paper, we propose a stochastic FOL using ego-centric images and motions (FOLe) that are generic information obtained from an autonomous agent. The proposed network consists of two staged sub-networks, including a future candidate network (FCN) and a future decision network (FDN) for localization. The FCN is used to generate several hypotheses to inform where an object will probably appear in an image according to its attribution. Our network directly produces the hypotheses in the future using only the ego-centric images and motions, which is trained with an end-to-end manner. The FDN predicts a multi-modal distribution based on the previous results of the FCN and determine the final location by maximizing the probability distribution. Experimental results demonstrate that the proposed model provides a superior performance to the state-of-the-art studies in nuScenes dataset.
Multidimensional Webshell Detection Method Based on Deep Learning

Webshell is a kind of web backdoor. Aiming at the problems of weak universality and easy to be bypassed in current Webshell detection, this paper proposes a multidimensional detection method of Webshell based on deep learning, and expounds the key technologies of its implementation. This method detects Webshell from different dimensions such as traffic, log and page. For the unencrypted traffic, Extracting the feature fields in the HTTP(HyperText Transfer Protocol) message from the unencrypted traffic data, use the BERT(Bi-directional Encoder Representations from Transformers) model to generate word vectors, and use the trained convolutional neural network model for predict. For the encrypted traffic, Extract log information and page relevance and other information for comprehensive modeling analysis. This method can solve the problem of escape detection after encryption confusion of Webshell. Thus facilitating the discovery of unknown Webshell.
A Study on Low-Latency Recognition-Synthesis-Based Any-to-One Voice Conversion

Some application scenarios of voice conversion, such as identity disguise in voice communication, require low-latency generation of converted speech. In traditional conversion methods, both history and future information in input speech are utilized to predict the converted acoustic features at each frame, which leads to long latency of voice conversion. Therefore, this paper proposes a low-latency recognition-synthesis-based any-to-one voice conversion method. Bottleneck (BN) features are extracted by an automatic speech recognition (ASR) acoustic model for frame-by-frame phoneme classification. A minimum mutual information (MMI) loss is introduced to reduce the speaker information in BNs caused by the low-latency configuration. The BN features are sent into a speaker-dependent low-latency LSTM-based acoustic feature predictor and the speech waveforms are reconstructed by an LPCNet vocoder from predicted acoustic features. The total latency of our proposed voice conversion method is 190ms, which is less than the delay requirement for comfortable communication in ITU-T G.114. The naturalness of converted speech is comparable with the upper-bound model trained without low-latency constraints.
Shape-bias Evaluation of Pretrained Models using Image Decomposition

Convolutional neural networks (CNNs) consist of convolutions that calculate correlations with surrounding pixels. Hence, they tend to analyze images by focusing on local textures rather than global shapes. Such texture bias may degrade the accuracy in recognizing shape-dominated images. In this paper, to compensate for this characteristic of CNNs and to increase the bias toward shape rather than texture, we introduce a training dataset by using image decomposition. Then, we build a CNN with shape bias by training on that dataset. Compared to CNNs trained using conventional ImageNet, we show that we can bias the network toward shape without significantly degrading the recognition accuracy; we also demonstrate that the CNN created using our proposed method obtains higher recognition accuracy than conventional methods in recognizing shape-dominated images.
A Light CNN with Split Batch Normalization for Spoofed Speech Detection Using Data Augmentation

The vulnerability of automatic speaker verification (ASV) is exposed to the threat of rapidly developing speech synthesis and voice conversion techniques. Developing anti-spoofing systems is an urgent need. This paper proposes a novel spoofed speech detection model for better utilizing the augmented data at the training stage. This model adopts a light convolutional neural network (LCNN) with the split batch normalization (SBN) structure to alleviate the issue of data pollution caused by data augmentation. The pre-trained wav2vec 2.0 model is used to extract features from input speech waveforms. Three data augmentation strategies, including audio compression, mixup and channel simulation, are compared in our experiments. Experimental results demonstrate that our proposed method achieves the state-of-the-art equal error rate (ERR) of 0.258% on the ASVspoof2019 LA task. Further analysis also confirms the effectiveness of the pre-trained model for feature extraction, the data augmentation strategies, and our proposed SBNLCNN model on improving the performance of spoofed speech detection.
Chatbot use for pre-triage procedures: a case study at a free-service university dental clinic

Chatbots or conversational agents’ tools have become important means of communication, especially with the needs arising from COVID-19 and the increase in artificial intelligence solutions. This work presents a case study of the use of a chatbot, specifically the WhatsApp messaging application, to assist in the pre-triage sector of a university dental clinic. To this end, several chatbot solutions were compared and the DialogFlow tool was chosen, which employs Machine Learning techniques to create the chatbot. The Twillio cloud communication platform was used to make the connection with the social network. The PSSUQ usability questionnaire (15-people sample space), was used to assess the satisfaction of chatbot users, analyze its responsiveness and possible failures, as well as verify if the bot achieves its initial objective and analyze its behavior.
Semantics-Guided Knowledge Integration for Domain Adaptation Few-shot Relation Extraction

Few-shot relation extraction achieves great progress by incorporating meta-learning with knowledge. Most of the recent works focus on integrating different types of knowledge, which ignored that there would be a huge knowledge gap in the application, especially facing domain adaptation. In this paper, we explore the effective way to integrate different forms of knowledge for domain adaptation few-shot relation extraction, and we face up to the problem of knowledge gap between training and testing. To be specific, we take the entity concept as the integrated knowledge, and the concept representation can be obtained from two sources, including textual label and concept graph. For the textual label, we evaluate the model's perception of concepts in different types of text fusion. For the concept graph, we pursue an effective integration method to adapt the modal gap and knowledge gap. Extensive experiments on the Few Rei domain adaptation dataset show that textual knowledge with language models and graph knowledge with a distance scorer are easy for transferring, and semantic information can effectively guide the integration process.
An Improved IRNet for Instance Segmentation Based on Image-level Supervision

To reduce the dependence on high-cost pixel-level annotations, instance segmentation based on image-level annotations have received researchers' attention. Inter-pixel Relation Network (IRNet) is one of the representative methods which obtain the final instance segmentation results by exploiting inter-pixel relations in Class Activation Maps (CAMs). In this paper, we introduce a contrastive loss function to improve the displacement field prediction results and a Siamese network is developed to obtain scale-invariant class boundary maps. With the improved displacement field and class boundary maps, the final segmentation results have been improved. Compared with the baseline IRNet, the mean average precision has increased by 3.2% on the PASCAL VOC 2012 val set.
A comparison of Generative Adversarial Networks for image super-resolution

This article presents a comparison of Generative Adversarial Networks for the image super-resolution problem. This is a relevant problem in several research areas and many real-world applications. The research consists of four steps: selecting successful Generative Adversarial Networks architectures, implementing two promising models, evaluating their image quality results, and analyzing their transfer learning capabilities. The main results indicate that both models are able to compute accurate results, with a reasonable deviation from state-of-the-art results and good transfer capabilities.
Significance of Quadrature and In-Phase Components for Synthetic Spoofed Speech Detection

For synthetic and voice converted Spoofed Speech Detection (SSD), Instantaneous Frequency (IF)-based features either exploit Hilbert Transform (HT) or Teager-based Energy Separation Algorithm (ESA) to estimate IF. However, HT-based approach leads to poor resolution in time-domain, and ESA-based approach leads to the lack of relative phase information. To that effect, we propose CFCCIF-QESA feature set, which encompasses excellent time resolution as well as relative phase information. Hence, we illustrate the significance of incorporating quadrature-phase component along with in-phase component for SSD of synthetic and voice converted spoof. The proposed feature set is evaluated using various performance metrics, namely, EER, MCC, F-measure, J-statistic, Jaccard Index, and Hamming loss. CFCCIF-QESA achieves a relative decrease in Average EER (AEER) by 22.53% w.r.t. the performance of CFCIF-ESA, for all the attacks in the dataset. Furthermore, results w.r.t. S10 attack (i.e., MaryTTS using unit selection synthesis), which is the most difficult to detect attack, show that CFCCIF-QESA achieves a 29.16% relative decrease in EER as compared to the CFCCIF-ESA. Furthermore, using model-level measures such as, Kullback Leibler Divergence (KLD) and Jenson Shannon Divergence (JSD), we also show the better discriminative ability of our proposed feature set CFCCIF-QESA than the existing CFCCIF-ESA. Finally, the analysis of the latency period for CFCCIF-QESA and CFCCIF-ESA is presented, and it shows better suitability of CFCCIF-QESA w.r.t. deployment in practical SSD systems.
Adapted Spectrogram Transformer for Unsupervised Cross-Domain Acoustic Anomaly Detection

Anomaly detection models can help to automatically and proactively detect faults in industrial machines. Microphones are appealing as they are generally inexpensive and unlike visual inspection, recording sound samples can give information about the internals of the machine. However, conventional methods based on an AutoEncoder (AE) structure learned from scratch generally struggle to learn how to robustly reconstruct samples with limited available data. This paper addresses this problem by presenting a method for unsupervised Acoustic Anomaly Detection (AAD) that adapts intermediate embeddings from a pretrained, self-attention-based spectrogram transformer. Transfer learning from a large, successful model offers a solution to learning with limited data by reusing external knowledge. For AAD, this can help to recognize subtle anomalies. This work proposes two method variants that take advantage of Intermediate Feature Embeddings (IFEs) from the Audio Spectrogram Transformer (AST). The first fits a Gaussian Mixture Model (GMM) on the IFEs produced by intermediate layers of the AST. We call this ADIFAST: Anomaly Detection from Intermediate Features extracted from AST. The second uses the IFEs in a different, more effective way by adapting the AST to an AE structure. We call it TELD: Transformer Encoder Linear Decoder network. The relationship between the two method variants is that they both take advantage of the IFEs extracted by the AST. Evaluating TELD on task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 challenge gives an average improvement to the Area Under Curve (AUC) score of 3.9% for binary labeling normal and anomalous samples in the target domain.